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  Subjects -> COMPUTER SCIENCE (Total: 2050 journals)
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    - COMPUTER SCIENCE (1196 journals)
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COMPUTER SCIENCE (1196 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 20)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 22)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 11)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 17)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 14)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 5)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 3)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 7)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 7)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 8)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 27)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 28)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 18)
Advances in Computer Science : an International Journal     Open Access   (Followers: 15)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 6)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 43)
Advances in Science and Research (ASR)     Open Access   (Followers: 4)
Advances in Technology Innovation     Open Access   (Followers: 5)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
AI EDAM     Hybrid Journal  
Air, Soil & Water Research     Open Access   (Followers: 11)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 5)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 34)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 132)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 2)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 283)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 43)
British Journal of Educational Technology     Hybrid Journal   (Followers: 144)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 20)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 2)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 11)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 15)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 94)
Computer Aided Surgery     Hybrid Journal   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 23)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 19)

        1 2 3 4 5 6 | Last

Journal Cover Algorithms
  [SJR: 0.357]   [H-I: 17]   [11 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1999-4893
   Published by MDPI Homepage  [198 journals]
  • Algorithms, Vol. 11, Pages 34: Failure Mode and Effects Analysis
           Considering Consensus and Preferences Interdependence

    • Authors: Jianghong Zhu, Rui Wang, Yanlai Li
      First page: 34
      Abstract: Failure mode and effects analysis is an effective and powerful risk evaluation technique in the field of risk management, and it has been extensively used in various industries for identifying and decreasing known and potential failure modes in systems, processes, products, and services. Traditionally, a risk priority number is applied to capture the ranking order of failure modes in failure mode and effects analysis. However, this method has several drawbacks and deficiencies, which need to be improved for enhancing its application capability. For instance, this method ignores the consensus-reaching process and the correlations among the experts’ preferences. Therefore, the aim of this study was to present a new risk priority method to determine the risk priority of failure modes under an interval-valued Pythagorean fuzzy environment, which combines the extended Geometric Bonferroni mean operator, a consensus-reaching process, and an improved Multi-Attributive Border Approximation area Comparison approach. Finally, a case study concerning product development is described to demonstrate the feasibility and effectiveness of the proposed method. The results show that the risk priority of failure modes obtained by the proposed method is more reasonable in practical application compared with other failure mode and effects analysis methods.
      Citation: Algorithms
      PubDate: 2018-03-21
      DOI: 10.3390/a11040034
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 35: Entropy-Based Algorithm for Supply-Chain
           Complexity Assessment

    • Authors: Boris Kriheli, Eugene Levner
      First page: 35
      Abstract: This paper considers a graph model of hierarchical supply chains. The goal is to measure the complexity of links between different components of the chain, for instance, between the principal equipment manufacturer (a root node) and its suppliers (preceding supply nodes). The information entropy is used to serve as a measure of knowledge about the complexity of shortages and pitfalls in relationship between the supply chain components under uncertainty. The concept of conditional (relative) entropy is introduced which is a generalization of the conventional (non-relative) entropy. An entropy-based algorithm providing efficient assessment of the supply chain complexity as a function of the SC size is developed.
      Citation: Algorithms
      PubDate: 2018-03-24
      DOI: 10.3390/a11040035
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 36: A Gradient-Based Cuckoo Search Algorithm
           for a Reservoir-Generation Scheduling Problem

    • Authors: Yu Feng, Jianzhong Zhou, Li Mo, Chao Wang, Zhe Yuan, Jiang Wu
      First page: 36
      Abstract: In this paper, a gradient-based cuckoo search algorithm (GCS) is proposed to solve a reservoir-scheduling problem. The classical cuckoo search (CS) is first improved by a self-adaptive solution-generation technique, together with a differential strategy for Lévy flight. This improved CS is then employed to solve the reservoir-scheduling problem, and a two-way solution-correction strategy is introduced to handle variants’ constraints. Moreover, a gradient-based search strategy is developed to improve the search speed and accuracy. Finally, the proposed GCS is used to obtain optimal schemes for cascade reservoirs in the Jinsha River, China. Results show that the mean and standard deviation of power generation obtained by GCS are much better than other methods. The converging speed of GCS is also faster. In the optimal results, the fluctuation of the water level obtained by GCS is small, indicating the proposed GCS’s effectiveness in dealing with reservoir-scheduling problems.
      Citation: Algorithms
      PubDate: 2018-03-25
      DOI: 10.3390/a11040036
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 37: Generalized Kinetic Monte Carlo Framework
           for Organic Electronics

    • Authors: Waldemar Kaiser, Johannes Popp, Michael Rinderle, Tim Albes, Alessio Gagliardi
      First page: 37
      Abstract: In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic material by the use of a non-cubic, generalized Voronoi tessellation and a model that connects sites to polymer chains. Herewith, we obtain a realistic model for both amorphous and crystalline domains of small molecules and polymers. Furthermore, we generalize the excitonic processes and include triplet exciton dynamics, which allows an enhanced investigation of OSCs and OLEDs. We outline the developed methods of our generalized kMC framework and give two exemplary studies of electrical and optical properties inside an organic semiconductor.
      Citation: Algorithms
      PubDate: 2018-03-26
      DOI: 10.3390/a11040037
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 38: Combinatorial GVNS (General Variable
           Neighborhood Search) Optimization for Dynamic Garbage Collection

    • Authors: Christos Papalitsas, Panayiotis Karakostas, Theodore Andronikos, Spyros Sioutas, Konstantinos Giannakis
      First page: 38
      Abstract: General variable neighborhood search (GVNS) is a well known and widely used metaheuristic for efficiently solving many NP-hard combinatorial optimization problems. We propose a novel extension of the conventional GVNS. Our approach incorporates ideas and techniques from the field of quantum computation during the shaking phase. The travelling salesman problem (TSP) is a well known NP-hard problem which has broadly been used for modelling many real life routing cases. As a consequence, TSP can be used as a basis for modelling and finding routes via the Global Positioning System (GPS). In this paper, we examine the potential use of this method for the GPS system of garbage trucks. Specifically, we provide a thorough presentation of our method accompanied with extensive computational results. The experimental data accumulated on a plethora of TSP instances, which are shown in a series of figures and tables, allow us to conclude that the novel GVNS algorithm can provide an efficient solution for this type of geographical problem.
      Citation: Algorithms
      PubDate: 2018-03-27
      DOI: 10.3390/a11040038
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 39: On Hierarchical Text
           Language-Identification Algorithms

    • Authors: Maimaitiyiming Hasimu, Wushour Silamu
      First page: 39
      Abstract: Text on the Internet is written in different languages and scripts that can be divided into different language groups. Most of the errors in language identification occur with similar languages. To improve the performance of short-text language identification, we propose four different levels of hierarchical language identification methods and conducted comparative tests in this paper. The efficiency of the algorithms was evaluated on sentences from 97 languages, and its macro-averaged F1-score reached in four-stage language identification was 0.9799. The experimental results verified that, after script identification, language group identification and similar language group identification, the performance of the language identification algorithm improved with each stage. Notably, the language identification accuracy between similar languages improved substantially. We also investigated how foreign content in a language affects language identification.
      Citation: Algorithms
      PubDate: 2018-03-27
      DOI: 10.3390/a11040039
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 40: Connectivity and Hamiltonicity of Canonical
           Colouring Graphs of Bipartite and Complete Multipartite Graphs

    • Authors: Ruth Haas, Gary MacGillivray
      First page: 40
      Abstract: A k-colouring of a graph G with colours 1 , 2 , … , k is canonical with respect to an ordering π = v 1 , v 2 , … , v n of the vertices of G if adjacent vertices are assigned different colours and, for 1 ≤ c ≤ k , whenever colour c is assigned to a vertex v i , each colour less than c has been assigned to a vertex that precedes v i in π . The canonical k-colouring graph of G with respect to π is the graph Can k π ( G ) with vertex set equal to the set of canonical k-colourings of G with respect to π , with two of these being adjacent if and only if they differ in the colour assigned to exactly one vertex. Connectivity and Hamiltonicity of canonical colouring graphs of bipartite and complete multipartite graphs is studied. It is shown that for complete multipartite graphs, and bipartite graphs there exists a vertex ordering π such that Can k π ( G ) is connected for large enough values of k. It is proved that a canonical colouring graph of a complete multipartite graph usually does not have a Hamilton cycle, and that there exists a vertex ordering π such that Can k π ( K m , n ) has a Hamilton path for all k ≥ 3 . The paper concludes with a detailed consideration of Can k π ( K 2 , 2 , … , 2 ) . For each k ≥ χ and all vertex orderings π , it is proved that Can k π ( K 2 , 2 , … , 2 ) is either disconnected or isomorphic to a particular tree.
      Citation: Algorithms
      PubDate: 2018-03-29
      DOI: 10.3390/a11040040
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 41: A Distributed Indexing Method for Timeline
           Similarity Query

    • Authors: Zhenwen He, Xiaogang Ma
      First page: 41
      Abstract: Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most of these data can be managed in the way of timelines. However, it is still a challenge to effectively and efficiently store, query, and process big timeline data, especially the instant recommendation based on timeline similarities. Most existing studies have focused on indexing spatial and interval datasets rather than the timeline dataset. In addition, many of them are designed for a centralized system. A timeline index structure adapting to parallel and distributed computation framework is in urgent need. In this research, we have defined the timeline similarity query and developed a novel timeline index in the distributed system, called the Distributed Triangle Increment Tree (DTI-Tree), to support the similarity query. The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with the Apache Spark. Furthermore, we have provided an open source timeline benchmark data generator, named TimelineGenerator, to generate various timeline test datasets for different conditions. The experiments for DTI-Tree’s construction, insertion, deletion, and similarity queries have been executed on a cluster with two benchmark datasets that are generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data.
      Citation: Algorithms
      PubDate: 2018-03-30
      DOI: 10.3390/a11040041
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 42: Learning Algorithm of Boltzmann Machine
           Based on Spatial Monte Carlo Integration Method

    • Authors: Muneki Yasuda
      First page: 42
      Abstract: The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order) spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE) method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.
      Citation: Algorithms
      PubDate: 2018-04-04
      DOI: 10.3390/a11040042
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 43: Near-Optimal Heuristics for Just-In-Time
           Jobs Maximization in Flow Shop Scheduling

    • Authors: Helio Fuchigami, Ruhul Sarker, Socorro Rangel
      First page: 43
      Abstract: The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average) while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines), with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop) for the considered problem.
      Citation: Algorithms
      PubDate: 2018-04-06
      DOI: 10.3390/a11040043
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 44: Safe Path Planning of Mobile Robot Based on
           Improved A* Algorithm in Complex Terrains

    • Authors: Hong-Mei Zhang, Ming-Long Li, Le Yang
      First page: 44
      Abstract: The A* algorithm has been widely investigated and applied in path planning problems, but it does not fully consider the safety and smoothness of the path. Therefore, an improved A* algorithm is presented in this paper. Firstly, a new environment modeling method is proposed in which the evaluation function of A* algorithm is improved by taking the safety cost into account. This results in a safer path which can stay farther away from obstacles. Then a new path smoothing method is proposed, which introduces a path evaluation mechanism into the smoothing process. This method is then applied to smoothing the path without safety reduction. Secondly, with respect to path planning problems in complex terrains, a complex terrain environment model is established in which the distance and safety cost of the evaluation function of the A* algorithm are converted into time cost. This results in a unification of units as well as a clarity in their physical meanings. The simulation results show that the improved A* algorithm can greatly improve the safety and smoothness of the planned path and the movement time of the robot in complex terrain is greatly reduced.
      Citation: Algorithms
      PubDate: 2018-04-09
      DOI: 10.3390/a11040044
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 45: Approximation Algorithms for the Geometric
           Firefighter and Budget Fence Problems

    • Authors: Rolf Klein, Christos Levcopoulos, Andrzej Lingas
      First page: 45
      Abstract: Let R denote a connected region inside a simple polygon, P. By building barriers (typically straight-line segments) in P \ R , we want to separate from R part(s) of P of maximum area. All edges of the boundary of P are assumed to be already constructed or natural barriers. In this paper we introduce two versions of this problem. In the budget fence version the region R is static, and there is an upper bound on the total length of barriers we may build. In the basic geometric firefighter version we assume that R represents a fire that is spreading over P at constant speed (varying speed can also be handled). Building a barrier takes time proportional to its length, and each barrier must be completed before the fire arrives. In this paper we are assuming that barriers are chosen from a given set B that satisfies certain conditions. Even for simple cases (e.g., P is a convex polygon and B the set of all diagonals), both problems are shown to be NP-hard. Our main result is an efficient ≈11.65 approximation algorithm for the firefighter problem, where the set B of allowed barriers is any set of straight-line segments with all endpoints on the boundary of P and pairwise disjoint interiors. Since this algorithm solves a much more general problem—a hybrid of scheduling and maximum coverage—it may find wider applications. We also provide a polynomial-time approximation scheme for the budget fence problem, for the case where barriers chosen from a set of straight-line cuts of the polygon must not cross.
      Citation: Algorithms
      PubDate: 2018-04-11
      DOI: 10.3390/a11040045
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 46: Short-Run Contexts and Imperfect Testing
           for Continuous Sampling Plans

    • Authors: Mirella Rodriguez, Daniel Jeske
      First page: 46
      Abstract: Continuous sampling plans are used to ensure a high level of quality for items produced in long-run contexts. The basic idea of these plans is to alternate between 100% inspection and a reduced rate of inspection frequency. Any inspected item that is found to be defective is replaced with a non-defective item. Because not all items are inspected, some defective items will escape to the customer. Analytical formulas have been developed that measure both the customer perceived quality and also the level of inspection effort. The analysis of continuous sampling plans does not apply to short-run contexts, where only a finite-size batch of items is to be produced. In this paper, a simulation algorithm is designed and implemented to analyze the customer perceived quality and the level of inspection effort for short-run contexts. A parameter representing the effectiveness of the test used during inspection is introduced to the analysis, and an analytical approximation is discussed. An application of the simulation algorithm that helped answer questions for the U.S. Navy is discussed.
      Citation: Algorithms
      PubDate: 2018-04-12
      DOI: 10.3390/a11040046
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 47: A Novel Dynamic Generalized
           Opposition-Based Grey Wolf Optimization Algorithm

    • Authors: Yanzhen Xing, Donghui Wang, Leiou Wang
      First page: 47
      Abstract: To enhance the convergence speed and calculation precision of the grey wolf optimization algorithm (GWO), this paper proposes a dynamic generalized opposition-based grey wolf optimization algorithm (DOGWO). A dynamic generalized opposition-based learning strategy enhances the diversity of search populations and increases the potential of finding better solutions which can accelerate the convergence speed, improve the calculation precision, and avoid local optima to some extent. Furthermore, 23 benchmark functions were employed to evaluate the DOGWO algorithm. Experimental results show that the proposed DOGWO algorithm could provide very competitive results compared with other analyzed algorithms, with a faster convergence speed, higher calculation precision, and stronger stability.
      Citation: Algorithms
      PubDate: 2018-04-13
      DOI: 10.3390/a11040047
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 48: An Approach for Setting Parameters for
           Two-Degree-of-Freedom PID Controllers

    • Authors: Xinxin Wang, Xiaoqiang Yan, Donghai Li, Li Sun
      First page: 48
      Abstract: In this paper, a new tuning method is proposed, based on the desired dynamics equation (DDE) and the generalized frequency method (GFM), for a two-degree-of-freedom proportional-integral-derivative (PID) controller. The DDE method builds a quantitative relationship between the performance and the two-degree-of-freedom PID controller parameters and guarantees the desired dynamic, but it cannot guarantee the stability margin. So, we have developed the proposed tuning method, which guarantees not only the desired dynamic but also the stability margin. Based on the DDE and the GFM, several simple formulas are deduced to calculate directly the controller parameters. In addition, it performs almost no overshooting setpoint response. Compared with Panagopoulos’ method, the proposed methodology is proven to be effective.
      Citation: Algorithms
      PubDate: 2018-04-13
      DOI: 10.3390/a11040048
      Issue No: Vol. 11, No. 4 (2018)
       
  • Algorithms, Vol. 11, Pages 24: A Class of Algorithms for Continuous
           Wavelet Transform Based on the Circulant Matrix

    • Authors: Hua Yi, Shi-You Xin, Jun-Feng Yin
      First page: 24
      Abstract: The Continuous Wavelet Transform (CWT) is an important mathematical tool in signal processing, which is a linear time-invariant operator with causality and stability for a fixed scale and real-life application. A novel and simple proof of the FFT-based fast method of linear convolution is presented by exploiting the structures of circulant matrix. After introducing Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT, a class of algorithms for continuous wavelet transform are proposed and analyzed in this paper, which can cover the algorithms in JLAB and WaveLab, as well as the other existing methods such as the c w t function in the toolbox of MATLAB. In this framework, two theoretical issues for the computation of CWT are analyzed. Firstly, edge effect is easily handled by using Equivalent Condition of Time-domain and Frequency-domain Algorithms of CWT and higher precision is expected. Secondly, due to the fact that linear convolution expands the support of the signal, which parts of the linear convolution are just the coefficients of CWT is analyzed by exploring the relationship of the filters of Frequency-domain and Time-domain algorithms, and some generalizations are given. Numerical experiments are presented to further demonstrate our analyses.
      Citation: Algorithms
      PubDate: 2018-02-27
      DOI: 10.3390/a11030024
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 25: Special Issue on Computational Intelligence
           and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern
           Recognition

    • Authors: Stefano Cagnoni, Mauro Castelli
      First page: 25
      Abstract: This special issue of Algorithms is devoted to the study of Computational Intelligence and Nature-Inspired Algorithms for Real-World Data Analytics and Pattern Recognition. The special issue considered both theoretical contributions able to advance the state-of-the-art in this field and practical applications that describe novel approaches for solving real-world problems.
      Citation: Algorithms
      PubDate: 2018-02-28
      DOI: 10.3390/a11030025
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 26: A Novel Evolutionary Algorithm for
           Designing Robust Analog Filters

    • Authors: Shaobo Li, Wang Zou, Jianjun Hu
      First page: 26
      Abstract: Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP) coupled with bond graph modeling. We applied our GP-based robust design (GPRD) algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA), our GPRD algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone. We also find that inappropriate GA tuning may mislead the search process and that multiple-simulation and perturbed fitness evaluation methods for evolving robustness have complementary behaviors with no absolute advantage of one over the other.
      Citation: Algorithms
      PubDate: 2018-03-01
      DOI: 10.3390/a11030026
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 27: Spectrum Allocation Based on an Improved
           Gravitational Search Algorithm

    • Authors: Liping Liu, Ning Wang, Zhigang Chen, Lin Guo
      First page: 27
      Abstract: In cognitive radio networks (CRNs), improving system utility and ensuring system fairness are two important issues. In this paper, we propose a spectrum allocation model to construct CRNs based on graph coloring theory, which contains three classes of matrices: available matrix, utility matrix, and interference matrix. Based on the model, we formulate a system objective function by jointly considering two features: system utility and system fairness. Based on the proposed model and the objective problem, we develop an improved gravitational search algorithm (IGSA) from two aspects: first, we introduce the pattern search algorithm (PSA) to improve the global optimization ability of the original gravitational search algorithm (GSA); second, we design the Chebyshev chaotic sequences to enhance the convergence speed and precision of the algorithm. Simulation results demonstrate that the proposed algorithm achieves better performance than traditional methods in spectrum allocation.
      Citation: Algorithms
      PubDate: 2018-03-05
      DOI: 10.3390/a11030027
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 28: Modified Convolutional Neural Network Based
           on Dropout and the Stochastic Gradient Descent Optimizer

    • Authors: Jing Yang, Guanci Yang
      First page: 28
      Abstract: This study proposes a modified convolutional neural network (CNN) algorithm that is based on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1) For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2) Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3) For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%.
      Citation: Algorithms
      PubDate: 2018-03-07
      DOI: 10.3390/a11030028
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 29: Dombi Aggregation Operators of Neutrosophic
           Cubic Sets for Multiple Attribute Decision-Making

    • Authors: Lilian Shi, Jun Ye
      First page: 29
      Abstract: The neutrosophic cubic set can describe complex decision-making problems with its single-valued neutrosophic numbers and interval neutrosophic numbers simultaneously. The Dombi operations have the advantage of good flexibility with the operational parameter. In order to solve decision-making problems with flexible operational parameter under neutrosophic cubic environments, the paper extends the Dombi operations to neutrosophic cubic sets and proposes a neutrosophic cubic Dombi weighted arithmetic average (NCDWAA) operator and a neutrosophic cubic Dombi weighted geometric average (NCDWGA) operator. Then, we propose a multiple attribute decision-making (MADM) method based on the NCDWAA and NCDWGA operators. Finally, we provide two illustrative examples of MADM to demonstrate the application and effectiveness of the established method.
      Citation: Algorithms
      PubDate: 2018-03-08
      DOI: 10.3390/a11030029
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 30: Modified Cuckoo Search Algorithm with
           Variational Parameters and Logistic Map

    • Authors: Liping Liu, Xiaobo Liu, Ning Wang, Peijun Zou
      First page: 30
      Abstract: Cuckoo Search (CS) is a Meta-heuristic method, which exhibits several advantages such as easier to application and fewer tuning parameters. However, it has proven to very easily fall into local optimal solutions and has a slow rate of convergence. Therefore, we propose Modified cuckoo search algorithm with variational parameter and logistic map (VLCS) to ameliorate these defects. To balance the exploitation and exploration of the VLCS algorithm, we not only use the coefficient function to change step size α and probability of detection p a at next generation, but also use logistic map of each dimension to initialize host nest location and update the location of host nest beyond the boundary. With fifteen benchmark functions, the simulations demonstrate that the VLCS algorithm can over come the disadvantages of the CS algorithm.In addition, the VLCS algorithm is good at dealing with high dimension problems and low dimension problems.
      Citation: Algorithms
      PubDate: 2018-03-15
      DOI: 10.3390/a11030030
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 31: Bilayer Local Search Enhanced Particle
           Swarm Optimization for the Capacitated Vehicle Routing Problem

    • Authors: A. Ahmed, Ji Sun
      First page: 31
      Abstract: The classical capacitated vehicle routing problem (CVRP) is a very popular combinatorial optimization problem in the field of logistics and supply chain management. Although CVRP has drawn interests of many researchers, no standard way has been established yet to obtain best known solutions for all the different problem sets. We propose an efficient algorithm Bilayer Local Search-based Particle Swarm Optimization (BLS-PSO) along with a novel decoding method to solve CVRP. Decoding method is important to relate the encoded particle position to a feasible CVRP solution. In bilayer local search, one layer of local search is for the whole population in any iteration whereas another one is applied only on the pool of the best particles generated in different generations. Such searching strategies help the BLS-PSO to perform better than the existing proposals by obtaining best known solutions for most of the existing benchmark problems within very reasonable computational time. Computational results also show that the performance achieved by the proposed algorithm outperforms other PSO-based approaches.
      Citation: Algorithms
      PubDate: 2018-03-15
      DOI: 10.3390/a11030031
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 32: Inverse Properties in Neutrosophic Triplet
           Loop and Their Application to Cryptography

    • Authors: Temitope Jaiyeola, Florentin Smarandache
      First page: 32
      Abstract: This paper is the first study of the neutrosophic triplet loop (NTL) which was originally introduced by Floretin Smarandache. NTL originated from the neutrosophic triplet set X: a collection of triplets ( x , n e u t ( x ) , a n t i ( x ) ) for an x ∈ X which obeys some axioms (existence of neutral(s) and opposite(s)). NTL can be informally said to be a neutrosophic triplet group that is not associative. That is, a neutrosophic triplet group is an NTL that is associative. In this study, NTL with inverse properties such as: right inverse property (RIP), left inverse property (LIP), right cross inverse property (RCIP), left cross inverse property (LCIP), right weak inverse property (RWIP), left weak inverse property (LWIP), automorphic inverse property (AIP), and anti-automorphic inverse property are introduced and studied. The research was carried out with the following assumptions: the inverse property (IP) is the RIP and LIP, cross inverse property (CIP) is the RCIP and LCIP, weak inverse property (WIP) is the RWIP and LWIP. The algebraic properties of neutrality and opposite in the aforementioned inverse property NTLs were investigated, and they were found to share some properties with the neutrosophic triplet group. The following were established: (1) In a CIPNTL (IPNTL), RIP (RCIP) and LIP (LCIP) were equivalent; (2) In an RIPNTL (LIPNTL), the CIP was equivalent to commutativity; (3) In a commutative NTL, the RIP, LIP, RCIP, and LCIP were found to be equivalent; (4) In an NTL, IP implied anti-automorphic inverse property and WIP, RCIP implied AIP and RWIP, while LCIP implied AIP and LWIP; (5) An NTL has the IP (CIP) if and only if it has the WIP and anti-automorphic inverse property (AIP); (6) A CIPNTL or an IPNTL was a quasigroup; (7) An LWIPNTL (RWIPNTL) was a left (right) quasigroup. The algebraic behaviours of an element, its neutral and opposite in the associator and commutator of a CIPNTL or an IPNTL were investigated. It was shown that ( Z p , ∗ ) where x ∗ y = ( p − 1 ) ( x + y ) , for any prime p, is a non-associative commutative CIPNTL and IPNTL. The application of some of these varieties of inverse property NTLs to cryptography is discussed.
      Citation: Algorithms
      PubDate: 2018-03-16
      DOI: 10.3390/a11030032
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 33: An Online Energy Management Control for
           Hybrid Electric Vehicles Based on Neuro-Dynamic Programming

    • Authors: Feiyan Qin, Weimin Li, Yue Hu, Guoqing Xu
      First page: 33
      Abstract: Hybrid electric vehicles are a compromise between traditional vehicles and pure electric vehicles and can be part of the solution to the energy shortage problem. Energy management strategies (EMSs) are highly related to energy utilization in HEVs’ fuel economy. In this research, we have employed a neuro-dynamic programming (NDP) method to simultaneously optimize fuel economy and battery state of charge (SOC). In this NDP method, the critic network is a multi-resolution wavelet neural network based on the Meyer wavelet function, and the action network is a conventional wavelet neural network based on the Morlet function. The weights and parameters of both networks are obtained by an algorithm of backpropagation type. The NDP-based EMS has been applied to a parallel HEV and compared with a previously reported NDP EMS and a stochastic dynamic programing-based method. Simulation results under ADVISOR2002 have shown that the proposed NDP approach achieves better performance than both the methods. These indicate that the proposed NDP EMS, and the CWNN and MRWNN, are effective in approximating a nonlinear system.
      Citation: Algorithms
      PubDate: 2018-03-19
      DOI: 10.3390/a11030033
      Issue No: Vol. 11, No. 3 (2018)
       
  • Algorithms, Vol. 11, Pages 12: Nonlinear Modeling and Coordinate
           Optimization of a Semi-Active Energy Regenerative Suspension with an
           Electro-Hydraulic Actuator

    • Authors: Farong Kou, Jiafeng Du, Zhe Wang, Dong Li, Jianan Xu
      First page: 12
      Abstract: In order to coordinate the damping performance and energy regenerative performance of energy regenerative suspension, this paper proposes a structure of a vehicle semi-active energy regenerative suspension with an electro-hydraulic actuator (EHA). In light of the proposed concept, a specific energy regenerative scheme is designed and a mechanical properties test is carried out. Based on the test results, the parameter identification for the system model is conducted using a recursive least squares algorithm. On the basis of the system principle, the nonlinear model of the semi-active energy regenerative suspension with an EHA is built. Meanwhile, linear-quadratic-Gaussian control strategy of the system is designed. Then, the influence of the main parameters of the EHA on the damping performance and energy regenerative performance of the suspension is analyzed. Finally, the main parameters of the EHA are optimized via the genetic algorithm. The test results show that when a sinusoidal is input at the frequency of 2 Hz and the amplitude of 30 mm, the spring mass acceleration root meam square value of the optimized EHA semi-active energy regenerative suspension is reduced by 22.23% and the energy regenerative power RMS value is increased by 40.51%, which means that while meeting the requirements of vehicle ride comfort and driving safety, the energy regenerative performance is improved significantly.
      Citation: Algorithms
      PubDate: 2018-01-23
      DOI: 10.3390/a11020012
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 13: muMAB: A Multi-Armed Bandit Model for
           Wireless Network Selection

    • Authors: Stefano Boldrini, Luca De Nardis, Giuseppe Caso, Mai Le, Jocelyn Fiorina, Maria-Gabriella Di Benedetto
      First page: 13
      Abstract: Multi-armed bandit (MAB) models are a viable approach to describe the problem of best wireless network selection by a multi-Radio Access Technology (multi-RAT) device, with the goal of maximizing the quality perceived by the final user. The classical MAB model does not allow, however, to properly describe the problem of wireless network selection by a multi-RAT device, in which a device typically performs a set of measurements in order to collect information on available networks, before a selection takes place. The MAB model foresees in fact only one possible action for the player, which is the selection of one among different arms at each time step; existing arm selection algorithms thus mainly differ in the rule according to which a specific arm is selected. This work proposes a new MAB model, named measure-use-MAB (muMAB), aiming at providing a higher flexibility, and thus a better accuracy in describing the network selection problem. The muMAB model extends the classical MAB model in a twofold manner; first, it foresees two different actions: to measure and to use; second, it allows actions to span over multiple time steps. Two new algorithms designed to take advantage of the higher flexibility provided by the muMAB model are also introduced. The first one, referred to as measure-use-UCB1 (muUCB1) is derived from the well known UCB1 algorithm, while the second one, referred to as Measure with Logarithmic Interval (MLI), is appositely designed for the new model so to take advantage of the new measure action, while aggressively using the best arm. The new algorithms are compared against existing ones from the literature in the context of the muMAB model, by means of computer simulations using both synthetic and captured data. Results show that the performance of the algorithms heavily depends on the Probability Density Function (PDF) of the reward received on each arm, with different algorithms leading to the best performance depending on the PDF. Results highlight, however, that as the ratio between the time required for using an arm and the time required to measure increases, the proposed algorithms guarantee the best performance, with muUCB1 emerging as the best candidate when the arms are characterized by similar mean rewards, and MLI prevailing when an arm is significantly more rewarding than others. This calls thus for the introduction of an adaptive approach capable of adjusting the behavior of the algorithm or of switching algorithm altogether, depending on the acquired knowledge on the PDF of the reward on each arm.
      Citation: Algorithms
      PubDate: 2018-01-26
      DOI: 10.3390/a11020013
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 14: An Optimal Online Resource Allocation
           Algorithm for Energy Harvesting Body Area Networks

    • Authors: Guangyuan Wu, Zhigang Chen, Lin Guo, Jia Wu
      First page: 14
      Abstract: In Body Area Networks (BANs), how to achieve energy management to extend the lifetime of the body area networks system is one of the most critical problems. In this paper, we design a body area network system powered by renewable energy, in which the sensors carried by patient with energy harvesting module can transmit data to a personal device. We do not require any a priori knowledge of the stochastic nature of energy harvesting and energy consumption. We formulate a user utility optimization problem. We use Lyapunov Optimization techniques to decompose the problem into three sub-problems, i.e., battery management, collecting rate control and transmission power allocation. We propose an online resource allocation algorithm to achieve two major goals: (1) balancing sensors’ energy harvesting and energy consumption while stabilizing the BANs system; and (2) maximizing the user utility. Performance analysis addresses required battery capacity, bounded data queue length and optimality of the proposed algorithm. Simulation results verify the optimization of algorithm.
      Citation: Algorithms
      PubDate: 2018-01-28
      DOI: 10.3390/a11020014
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 15: Modeling the Trend of Credit Card Usage
           Behavior for Different Age Groups Based on Singular Spectrum Analysis

    • Authors: Wei Nai, Lu Liu, Shaoyin Wang, Decun Dong
      First page: 15
      Abstract: Credit card holders from different age groups have different usage behaviors, so deeply investigating the credit card usage condition and properly modeling the usage trend of all customers in different age groups from time series data is meaningful for financial institutions as well as banks. Until now, related research in trend analysis of credit card usage has mostly been focused on specific group of people, such as the behavioral tendencies of the elderly or college students, or certain behaviors, such as the increasing number of cards owned and the rise in personal card debt or bankruptcy, in which the only analysis methods employed are simply enumerating or classifying raw data; thus, there is a lack of support in specific mathematical models based on usage behavioral time series data. Considering that few systematic modeling methods have been introduced, in this paper, a novel usage trend analysis method for credit card holders in different age groups based on singular spectrum analysis (SSA) has been proposed, using the time series data from the Survey of Consumer Payment Choice (SCPC). The decomposition and reconstruction process in the method is proposed. The results show that the credit card usage frequency falls down from the age of 26 to the lowest point at around the age of 58 and then begins to increase again. At last, future work is discussed.
      Citation: Algorithms
      PubDate: 2018-01-29
      DOI: 10.3390/a11020015
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 16: A Novel Spectrum Scheduling Scheme with Ant
           Colony Optimization Algorithm

    • Authors: Liping Liu, Ning Wang, Zhigang Chen, Lin Guo
      First page: 16
      Abstract: Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO) from two aspects: first, we introduce differential evolution (DE) process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS) process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.
      Citation: Algorithms
      PubDate: 2018-01-29
      DOI: 10.3390/a11020016
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 17: An Improved Bacterial-Foraging
           Optimization-Based Machine Learning Framework for Predicting the Severity
           of Somatization Disorder

    • Authors: Xinen Lv, Huiling Chen, Qian Zhang, Xujie Li, Hui Huang, Gang Wang
      First page: 17
      Abstract: It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO) to optimize two key parameters (penalty coefficient and the kernel width) of a kernel extreme learning machine (KELM) and build an IBFO-based KELM (IBFO-KELM) for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90) is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model), GA-KELM (model based on genetic algorithm), PSO-KELM (model based on particle swarm optimization algorithm) and Grid-KELM (model based on grid search method). The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC), sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.
      Citation: Algorithms
      PubDate: 2018-02-06
      DOI: 10.3390/a11020017
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 18: A New Greedy Insertion Heuristic Algorithm
           with a Multi-Stage Filtering Mechanism for Energy-Efficient Single Machine
           Scheduling Problems

    • Authors: Hongliang Zhang, Youcai Fang, Ruilin Pan, Chuanming Ge
      First page: 18
      Abstract: To improve energy efficiency and maintain the stability of the power grid, time-of-use (TOU) electricity tariffs have been widely used around the world, which bring both opportunities and challenges to the energy-efficient scheduling problems. Single machine scheduling problems under TOU electricity tariffs are of great significance both in theory and practice. Although methods based on discrete-time or continuous-time models have been put forward for addressing this problem, they are deficient in solution quality or time complexity, especially when dealing with large-size instances. To address large-scale problems more efficiently, a new greedy insertion heuristic algorithm with a multi-stage filtering mechanism including coarse granularity and fine granularity filtering is developed in this paper. Based on the concentration and diffusion strategy, the algorithm can quickly filter out many impossible positions in the coarse granularity filtering stage, and then, each job can find its optimal position in a relatively large space in the fine granularity filtering stage. To show the effectiveness and computational process of the proposed algorithm, a real case study is provided. Furthermore, two sets of contrast experiments are conducted, aiming to demonstrate the good application of the algorithm. The experiments indicate that the small-size instances can be solved within 0.02 s using our algorithm, and the accuracy is further improved. For the large-size instances, the computation speed of our algorithm is improved greatly compared with the classic greedy insertion heuristic algorithm.
      Citation: Algorithms
      PubDate: 2018-02-09
      DOI: 10.3390/a11020018
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 19: Common Nearest Neighbor Clustering—A
           Benchmark

    • Authors: Oliver Lemke, Bettina Keller
      First page: 19
      Abstract: Cluster analyses are often conducted with the goal to characterize an underlying probability density, for which the data-point density serves as an estimate for this probability density. We here test and benchmark the common nearest neighbor (CNN) cluster algorithm. This algorithm assigns a spherical neighborhood R to each data point and estimates the data-point density between two data points as the number of data points N in the overlapping region of their neighborhoods (step 1). The main principle in the CNN cluster algorithm is cluster growing. This grows the clusters by sequentially adding data points and thereby effectively positions the border of the clusters along an iso-surface of the underlying probability density. This yields a strict partitioning with outliers, for which the cluster represents peaks in the underlying probability density—termed core sets (step 2). The removal of the outliers on the basis of a threshold criterion is optional (step 3). The benchmark datasets address a series of typical challenges, including datasets with a very high dimensional state space and datasets in which the cluster centroids are aligned along an underlying structure (Birch sets). The performance of the CNN algorithm is evaluated with respect to these challenges. The results indicate that the CNN cluster algorithm can be useful in a wide range of settings. Cluster algorithms are particularly important for the analysis of molecular dynamics (MD) simulations. We demonstrate how the CNN cluster results can be used as a discretization of the molecular state space for the construction of a core-set model of the MD improving the accuracy compared to conventional full-partitioning models. The software for the CNN clustering is available on GitHub.
      Citation: Algorithms
      PubDate: 2018-02-09
      DOI: 10.3390/a11020019
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 20: Vertex Cover Reconfiguration and Beyond

    • Authors: Amer Mouawad, Naomi Nishimura, Venkatesh Raman, Sebastian Siebertz
      First page: 20
      Abstract: In the Vertex Cover Reconfiguration (VCR) problem, given a graph G, positive integers k and ℓ and two vertex covers S and T of G of size at most k, we determine whether S can be transformed into T by a sequence of at most ℓ vertex additions or removals such that every operation results in a vertex cover of size at most k. Motivated by results establishing the W [ 1 ] -hardness of VCR when parameterized by ℓ, we delineate the complexity of the problem restricted to various graph classes. In particular, we show that VCR remains W [ 1 ] -hard on bipartite graphs, is NP -hard, but fixed-parameter tractable on (regular) graphs of bounded degree and more generally on nowhere dense graphs and is solvable in polynomial time on trees and (with some additional restrictions) on cactus graphs.
      Citation: Algorithms
      PubDate: 2018-02-09
      DOI: 10.3390/a11020020
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 21: Research on Degeneration Model of Neural
           Network for Deep Groove Ball Bearing Based on Feature Fusion

    • Authors: Lijun Zhang, Junyu Tao
      First page: 21
      Abstract: Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM) model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.
      Citation: Algorithms
      PubDate: 2018-02-11
      DOI: 10.3390/a11020021
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 22: Design Optimization of Steering Mechanisms
           for Articulated Off-Road Vehicles Based on Genetic Algorithms

    • Authors: Chen Zhou, Xinhui Liu, Feixiang Xu
      First page: 22
      Abstract: Two cylinders arranged symmetrically on a frame have become a major form of steering mechanism for articulated off-road vehicles (AORVs). However, the differences of stroke and arm lead to pressure fluctuation, vibration noise, and a waste of torque. In this paper, the differences of stroke and arm are reduced based on a genetic algorithm (GA). First, the mathematical model of the steering mechanism is put forward. Then, the difference of stroke and arm are optimized using a GA. Finally, a FW50GLwheel loader is used as an example to demonstrate the proposed GA-based optimization method, and its effectiveness is verified by means of automatic dynamic analysis of mechanical systems (ADAMS). The stroke difference of the steering hydraulic cylinders was reduced by 92% and the arm difference reached a decrease of 78% through GA optimization, in comparison with unoptimized structures. The simulation result shows that the steering mechanism optimized by GA behaved better than by previous methods.
      Citation: Algorithms
      PubDate: 2018-02-13
      DOI: 10.3390/a11020022
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 23: Effects of Random Values for Particle Swarm
           Optimization Algorithm

    • Authors: Hou-Ping Dai, Dong-Dong Chen, Zhou-Shun Zheng
      First page: 23
      Abstract: Particle swarm optimization (PSO) algorithm is generally improved by adaptively adjusting the inertia weight or combining with other evolution algorithms. However, in most modified PSO algorithms, the random values are always generated by uniform distribution in the range of [0, 1]. In this study, the random values, which are generated by uniform distribution in the ranges of [0, 1] and [−1, 1], and Gauss distribution with mean 0 and variance 1 ( U [ 0 , 1 ] , U [ − 1 , 1 ] and G ( 0 , 1 ) ), are respectively used in the standard PSO and linear decreasing inertia weight (LDIW) PSO algorithms. For comparison, the deterministic PSO algorithm, in which the random values are set as 0.5, is also investigated in this study. Some benchmark functions and the pressure vessel design problem are selected to test these algorithms with different types of random values in three space dimensions (10, 30, and 100). The experimental results show that the standard PSO and LDIW-PSO algorithms with random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are more likely to avoid falling into local optima and quickly obtain the global optima. This is because the large-scale random values can expand the range of particle velocity to make the particle more likely to escape from local optima and obtain the global optima. Although the random values generated by U [ − 1 , 1 ] or G ( 0 , 1 ) are beneficial to improve the global searching ability, the local searching ability for a low dimensional practical optimization problem may be decreased due to the finite particles.
      Citation: Algorithms
      PubDate: 2018-02-15
      DOI: 10.3390/a11020023
      Issue No: Vol. 11, No. 2 (2018)
       
  • Algorithms, Vol. 11, Pages 2: Models for Multiple Attribute
           Decision-Making with Dual Generalized Single-Valued Neutrosophic
           Bonferroni Mean Operators

    • Authors: Jie Wang, Xiyue Tang, Guiwu Wei
      First page: 2
      Abstract: In this article, we expand the dual generalized weighted BM (DGWBM) and dual generalized weighted geometric Bonferroni mean (DGWGBM) operator with single valued neutrosophic numbers (SVNNs) to propose the dual generalized single-valued neutrosophic number WBM (DGSVNNWBM) operator and dual generalized single-valued neutrosophic numbers WGBM (DGSVNNWGBM) operator. Then, the multiple attribute decision making (MADM) methods are proposed with these operators. In the end, we utilize an applicable example for strategic suppliers selection to prove the proposed methods.
      Citation: Algorithms
      PubDate: 2018-01-05
      DOI: 10.3390/a11010002
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 3: Analytic Combinatorics for Computing Seeding
           Probabilities

    • Authors: Guillaume Filion
      First page: 3
      Abstract: Seeding heuristics are the most widely used strategies to speed up sequence alignment in bioinformatics. Such strategies are most successful if they are calibrated, so that the speed-versus-accuracy trade-off can be properly tuned. In the widely used case of read mapping, it has been so far impossible to predict the success rate of competing seeding strategies for lack of a theoretical framework. Here, we present an approach to estimate such quantities based on the theory of analytic combinatorics. The strategy is to specify a combinatorial construction of reads where the seeding heuristic fails, translate this specification into a generating function using formal rules, and finally extract the probabilities of interest from the singularities of the generating function. The generating function can also be used to set up a simple recurrence to compute the probabilities with greater precision. We use this approach to construct simple estimators of the success rate of the seeding heuristic under different types of sequencing errors, and we show that the estimates are accurate in practical situations. More generally, this work shows novel strategies based on analytic combinatorics to compute probabilities of interest in bioinformatics.
      Citation: Algorithms
      PubDate: 2018-01-10
      DOI: 10.3390/a11010003
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 4: Transform a Simple Sketch to a Chinese
           Painting by a Multiscale Deep Neural Network

    • Authors: Daoyu Lin, Yang Wang, Guangluan Xu, Jun Li, Kun Fu
      First page: 4
      Abstract: Recently, inspired by the power of deep learning, convolution neural networks can produce fantastic images at the pixel level. However, a significant limiting factor for previous approaches is that they focus on some simple datasets such as faces and bedrooms. In this paper, we propose a multiscale deep neural network to transform sketches into Chinese paintings. To synthesize more realistic imagery, we train the generative network by using both L1 loss and adversarial loss. Additionally, users can control the process of the synthesis since the generative network is feed-forward. This network can also be treated as neural style transfer by adding an edge detector. Furthermore, additional experiments on image colorization and image super-resolution demonstrate the universality of our proposed approach.
      Citation: Algorithms
      PubDate: 2018-01-11
      DOI: 10.3390/a11010004
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 5: Approaches to Multiple-Attribute
           Decision-Making Based on Pythagorean 2-Tuple Linguistic Bonferroni Mean
           Operators

    • Authors: Xiyue Tang, Yuhan Huang, Guiwu Wei
      First page: 5
      Abstract: In this paper, we investigate multiple-attribute decision-making (MADM) with Pythagorean 2-tuple linguistic numbers (P2TLNs). Then, we combine the weighted Bonferroni mean (WBM) operator and weighted geometric Bonferroni mean (WGBM) operator with P2TLNs to propose the Pythagorean 2-tuple linguistic WBM (P2TLWBM) operator and Pythagorean 2-tuple linguistic WGBM (P2TLWGBM) operator; MADM methods are then developed based on these two operators. Finally, a practical example for green supplier selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.
      Citation: Algorithms
      PubDate: 2018-01-12
      DOI: 10.3390/a11010005
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 6: A Novel Perceptual Hash Algorithm for
           Multispectral Image Authentication

    • Authors: Kaimeng Ding, Shiping Chen, Fan Meng
      First page: 6
      Abstract: The perceptual hash algorithm is a technique to authenticate the integrity of images. While a few scholars have worked on mono-spectral image perceptual hashing, there is limited research on multispectral image perceptual hashing. In this paper, we propose a perceptual hash algorithm for the content authentication of a multispectral remote sensing image based on the synthetic characteristics of each band: firstly, the multispectral remote sensing image is preprocessed with band clustering and grid partition; secondly, the edge feature of the band subsets is extracted by band fusion-based edge feature extraction; thirdly, the perceptual feature of the same region of the band subsets is compressed and normalized to generate the perceptual hash value. The authentication procedure is achieved via the normalized Hamming distance between the perceptual hash value of the recomputed perceptual hash value and the original hash value. The experiments indicated that our proposed algorithm is robust compared to content-preserved operations and it efficiently authenticates the integrity of multispectral remote sensing images.
      Citation: Algorithms
      PubDate: 2018-01-14
      DOI: 10.3390/a11010006
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 7: Optimization Design by Genetic Algorithm
           Controller for Trajectory Control of a 3-RRR Parallel Robot

    • Authors: Lianchao Sheng, Wei Li
      First page: 7
      Abstract: In order to improve the control precision and robustness of the existing proportion integration differentiation (PID) controller of a 3-Revolute–Revolute–Revolute (3-RRR) parallel robot, a variable PID parameter controller optimized by a genetic algorithm controller is proposed in this paper. Firstly, the inverse kinematics model of the 3-RRR parallel robot was established according to the vector method, and the motor conversion matrix was deduced. Then, the error square integral was chosen as the fitness function, and the genetic algorithm controller was designed. Finally, the control precision of the new controller was verified through the simulation model of the 3-RRR planar parallel robot—built in SimMechanics—and the robustness of the new controller was verified by adding interference. The results show that compared with the traditional PID controller, the new controller designed in this paper has better control precision and robustness, which provides the basis for practical application.
      Citation: Algorithms
      PubDate: 2018-01-15
      DOI: 10.3390/a11010007
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 8: On Application of the Ray-Shooting Method
           for LQR via Static-Output-Feedback

    • Authors: Yossi Peretz
      First page: 8
      Abstract: In this article we suggest a randomized algorithm for the LQR (Linear Quadratic Regulator) optimal-control problem via static-output-feedback. The suggested algorithm is based on the recently introduced randomized optimization method called the Ray-Shooting Method that efficiently solves the global minimization problem of continuous functions over compact non-convex unconnected regions. The algorithm presented here is a randomized algorithm with a proof of convergence in probability. Its practical implementation has good performance in terms of the quality of controllers obtained and the percentage of success.
      Citation: Algorithms
      PubDate: 2018-01-16
      DOI: 10.3390/a11010008
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 9: Application of a Hybrid Model Based on a
           Convolutional Auto-Encoder and Convolutional Neural Network in
           Object-Oriented Remote Sensing Classification

    • Authors: Wei Cui, Qi Zhou, Zhendong Zheng
      First page: 9
      Abstract: Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model; furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model.
      Citation: Algorithms
      PubDate: 2018-01-16
      DOI: 10.3390/a11010009
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 10: Inapproximability of Maximum Biclique
           Problems, Minimum k-Cut and Densest At-Least-k-Subgraph from the Small Set
           Expansion Hypothesis

    • Authors: Pasin Manurangsi
      First page: 10
      Abstract: The Small Set Expansion Hypothesis is a conjecture which roughly states that it is NP-hard to distinguish between a graph with a small subset of vertices whose (edge) expansion is almost zero and one in which all small subsets of vertices have expansion almost one. In this work, we prove conditional inapproximability results with essentially optimal ratios for the following graph problems based on this hypothesis: Maximum Edge Biclique, Maximum Balanced Biclique, Minimum k-Cut and Densest At-Least-k-Subgraph. Our hardness results for the two biclique problems are proved by combining a technique developed by Raghavendra, Steurer and Tulsiani to avoid locality of gadget reductions with a generalization of Bansal and Khot’s long code test whereas our results for Minimum k-Cut and Densest At-Least-k-Subgraph are shown via elementary reductions.
      Citation: Algorithms
      PubDate: 2018-01-17
      DOI: 10.3390/a11010010
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 11: Acknowledgement to Reviewers of Algorithms
           in 2017

    • Authors: Algorithms Editorial Office
      First page: 11
      Abstract: Peer review is an essential part in the publication process, ensuring that Algorithms maintains high quality standards for its published papers.[...]
      Citation: Algorithms
      PubDate: 2018-01-17
      DOI: 10.3390/a11010011
      Issue No: Vol. 11, No. 1 (2018)
       
  • Algorithms, Vol. 11, Pages 1: Iteration Scheme for Solving the System of
           Coupled Integro-Differential Equations for Excited and Ionized States of
           Molecular Systems

    • Authors: Anton Kasprzhitskii, Georgy Lazorenko, Victor Yavna
      First page: 1
      Abstract: Investigation of the interaction of electromagnetic radiation with molecular systems provides most of the information on their structure and properties. Interpretation of experimental data is directly determined by the knowledge of the structure of energy levels and its change in the transition of these systems to an excited state. A key task of the methods for calculating the molecular orbitals of excited states is to accurately describe the emerging vacancies of the molecular core, leading to radial relaxation of the electron density. We propose an iterative scheme for solving a system of coupled integro-differential equations for obtaining molecular orbitals of electron configurations with excited/ionized deep and subvalent shells in a single-center representation. The numerical procedure of the iterative scheme is reduced to solving a boundary value problem based on a combination of the three-point difference scheme of Numerov and Thomas algorithm. To increase the rate of convergence of the computational procedure, an accurate account is taken of the behavior of the electron density near the nuclei of the molecular system. The realization of the algorithm of the computational scheme is considered on the example of a diatomic hydrogen fluoride molecule. The energy characteristics of the ground and ionized states of the molecule are estimated, and also the spatial distribution of the electron density is presented for the example of the σ-symmetry shell.
      Citation: Algorithms
      PubDate: 2017-12-22
      DOI: 10.3390/a11010001
      Issue No: Vol. 11, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 109: Properties of Vector Embeddings in Social
           Networks

    • Authors: Fatemeh Salehi Rizi, Michael Granitzer, Konstantin Ziegler
      First page: 109
      Abstract: Embedding social network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification, node clustering, link prediction and network visualization. However, the information contained in these vector embeddings remains abstract and hard to interpret. Methods for inspecting embeddings usually rely on visualization methods, which do not work on a larger scale and do not give concrete interpretations of vector embeddings in terms of preserved network properties (e.g., centrality or betweenness measures). In this paper, we study and investigate network properties preserved by recent random walk-based embedding procedures like node2vec, DeepWalk or LINE. We propose a method that applies learning to rank in order to relate embeddings to network centralities. We evaluate our approach with extensive experiments on real-world and artificial social networks. Experiments show that each embedding method learns different network properties. In addition, we show that our graph embeddings in combination with neural networks provide a computationally efficient way to approximate the Closeness Centrality measure in social networks.
      Citation: Algorithms
      PubDate: 2017-09-27
      DOI: 10.3390/a10040109
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 110: DDC Control Techniques for Three-Phase
           BLDC Motor Position Control

    • Authors: Rana Masood, Dao Wang, Zain Ali, Babar Khan
      First page: 110
      Abstract: In this article, a novel hybrid control scheme is proposed for controlling the position of a three-phase brushless direct current (BLDC) motor. The hybrid controller consists of discrete time sliding mode control (SMC) with model free adaptive control (MFAC) to make a new data-driven control (DDC) strategy that is able to reduce the simulation time and complexity of a nonlinear system. The proposed hybrid algorithm is also suitable for controlling the speed variations of a BLDC motor, and is also applicable for the real time simulation of platforms such as a gimbal platform. The DDC method does not require any system model because it depends on data collected by the system about its Inputs/Outputs (IOS). However, the model-based control (MBC) method is difficult to apply from a practical point of view and is time-consuming because we need to linearize the system model. The above proposed method is verified by multiple simulations using MATLAB Simulink. It shows that the proposed controller has better performance, more precise tracking, and greater robustness compared with the classical proportional integral derivative (PID) controller, MFAC, and model free learning adaptive control (MFLAC).
      Citation: Algorithms
      PubDate: 2017-09-25
      DOI: 10.3390/a10040110
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 111: Game Theory-Inspired Evolutionary
           Algorithm for Global Optimization

    • Authors: Guanci Yang
      First page: 111
      Abstract: Many approaches that model specific intelligent behaviors perform excellently in solving complex optimization problems. Game theory is widely recognized as an important tool in many fields. This paper introduces a game theory-inspired evolutionary algorithm for global optimization (GameEA). A formulation to estimate payoff expectations is provided, which is a mechanism to make a player become a rational decision-maker. GameEA has one population (i.e., set of players) and generates new offspring only through an imitation operator and a belief-learning operator. An imitation operator adopts learning strategies and actions from other players to improve its competitiveness and applies these strategies to future games where one player updates its chromosome by strategically copying segments of gene sequences from a competitor. Belief learning refers to models in which a player adjusts his/her strategies, behavior or chromosomes by analyzing the current history information to improve solution quality. Experimental results on various classes of problems show that GameEA outperforms the other four algorithms on stability, robustness, and accuracy.
      Citation: Algorithms
      PubDate: 2017-09-30
      DOI: 10.3390/a10040111
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 112: Mapping Higher-Order Network Flows in
           Memory and Multilayer Networks with Infomap

    • Authors: Daniel Edler, Ludvig Bohlin, and Rosvall
      First page: 112
      Abstract: Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping higher-order network flows. However, complex systems come in many forms and demand a range of representations, including memory and multilayer networks, which in turn call for versatile community-detection algorithms to reveal important modular regularities in the flows. Here we show that various forms of higher-order network flows can be represented in a unified way with networks that distinguish physical nodes for representing a complex system’s objects from state nodes for describing flows between the objects. Moreover, these so-called sparse memory networks allow the information-theoretic community detection method known as the map equation to identify overlapping and nested flow modules in data from a range of different higher-order interactions such as multistep, multi-source, and temporal data. We derive the map equation applied to sparse memory networks and describe its search algorithm Infomap, which can exploit the flexibility of sparse memory networks. Together they provide a general solution to reveal overlapping modular patterns in higher-order flows through complex systems.
      Citation: Algorithms
      PubDate: 2017-09-30
      DOI: 10.3390/a10040112
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 113: Scale Reduction Techniques for Computing
           Maximum Induced Bicliques

    • Authors: Shahram Shahinpour, Shirin Shirvani, Zeynep Ertem, Sergiy Butenko
      First page: 113
      Abstract: Given a simple, undirected graph G, a biclique is a subset of vertices inducing a complete bipartite subgraph in G. In this paper, we consider two associated optimization problems, the maximum biclique problem, which asks for a biclique of the maximum cardinality in the graph, and the maximum edge biclique problem, aiming to find a biclique with the maximum number of edges in the graph. These NP-hard problems find applications in biclustering-type tasks arising in complex network analysis. Real-life instances of these problems often involve massive, but sparse networks. We develop exact approaches for detecting optimal bicliques in large-scale graphs that combine effective scale reduction techniques with integer programming methodology. Results of computational experiments with numerous real-life network instances demonstrate the performance of the proposed approach.
      Citation: Algorithms
      PubDate: 2017-10-04
      DOI: 10.3390/a10040113
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 114: Variable Selection in Time Series
           Forecasting Using Random Forests

    • Authors: Hristos Tyralis, Georgia Papacharalampous
      First page: 114
      Abstract: Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.
      Citation: Algorithms
      PubDate: 2017-10-04
      DOI: 10.3390/a10040114
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 115: A Multi-Threading Algorithm to Detect and
           Remove Cycles in Vertex- and Arc-Weighted Digraph

    • Authors: Huanqing Cui, Jian Niu, Chuanai Zhou, Minglei Shu
      First page: 115
      Abstract: A graph is a very important structure to describe many applications in the real world. In many applications, such as dependency graphs and debt graphs, it is an important problem to find and remove cycles to make these graphs be cycle-free. The common algorithm often leads to an out-of-memory exception in commodity personal computer, and it cannot leverage the advantage of multicore computers. This paper introduces a new problem, cycle detection and removal with vertex priority. It proposes a multithreading iterative algorithm to solve this problem for large-scale graphs on personal computers. The algorithm includes three main steps: simplification to decrease the scale of graph, calculation of strongly connected components, and cycle detection and removal according to a pre-defined priority in parallel. This algorithm avoids the out-of-memory exception by simplification and iteration, and it leverages the advantage of multicore computers by multithreading parallelism. Five different versions of the proposed algorithm are compared by experiments, and the results show that the parallel iterative algorithm outperforms the others, and simplification can effectively improve the algorithm's performance.
      Citation: Algorithms
      PubDate: 2017-10-10
      DOI: 10.3390/a10040115
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 116: Remote Sensing Image Enhancement Based on
           Non-Local Means Filter in NSCT Domain

    • Authors: Liangliang Li, Yujuan Si, Zhenhong Jia
      First page: 116
      Abstract: In this paper, a novel remote sensing image enhancement technique based on a non-local means filter in a nonsubsampled contourlet transform (NSCT) domain is proposed. The overall flow of the approach can be divided into the following steps: Firstly, the image is decomposed into one low-frequency sub-band and several high-frequency sub-bands with NSCT. Secondly, contrast stretching is adopted to deal with the low-frequency sub-band coefficients, and the non-local means filter is applied to suppress the noise contained in the first high-frequency sub-band coefficients. Thirdly, the processed coefficients are reconstructed with the inverse NSCT transform. Finally, the unsharp filter is used to enhance the details of the image. The simulation results show that the proposed algorithm has better performance in remote sensing image enhancement than the existing approaches.
      Citation: Algorithms
      PubDate: 2017-10-11
      DOI: 10.3390/a10040116
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 117: Fabric Weave Pattern and Yarn Color
           Recognition and Classification Using a Deep ELM Network

    • Authors: Babar Khan, Zhijie Wang, Fang Han, Ather Iqbal, Rana Masood
      First page: 117
      Abstract: Usually, a fabric weave pattern is recognized using methods which identify the warp floats and weft floats. Although these methods perform well for uniform or repetitive weave patterns, in the case of complex weave patterns, these methods become computationally complex and the classification error rates are comparatively higher. Furthermore, the fault-tolerance (invariance) and stability (selectivity) of the existing methods are still to be enhanced. We present a novel biologically-inspired method to invariantly recognize the fabric weave pattern (fabric texture) and yarn color from the color image input. We proposed a model in which the fabric weave pattern descriptor is based on the HMAX model for computer vision inspired by the hierarchy in the visual cortex, the color descriptor is based on the opponent color channel inspired by the classical opponent color theory of human vision, and the classification stage is composed of a multi-layer (deep) extreme learning machine. Since the weave pattern descriptor, yarn color descriptor, and the classification stage are all biologically inspired, we propose a method which is completely biologically plausible. The classification performance of the proposed algorithm indicates that the biologically-inspired computer-aided-vision models might provide accurate, fast, reliable and cost-effective solution to industrial automation.
      Citation: Algorithms
      PubDate: 2017-10-13
      DOI: 10.3390/a10040117
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 118: Iterative Parameter Estimation Algorithms
           for Dual-Frequency Signal Models

    • Authors: Siyu Liu, Ling Xu, Feng Ding
      First page: 118
      Abstract: This paper focuses on the iterative parameter estimation algorithms for dual-frequency signal models that are disturbed by stochastic noise. The key of the work is to overcome the difficulty that the signal model is a highly nonlinear function with respect to frequencies. A gradient-based iterative (GI) algorithm is presented based on the gradient search. In order to improve the estimation accuracy of the GI algorithm, a Newton iterative algorithm and a moving data window gradient-based iterative algorithm are proposed based on the moving data window technique. Comparative simulation results are provided to illustrate the effectiveness of the proposed approaches for estimating the parameters of signal models.
      Citation: Algorithms
      PubDate: 2017-10-14
      DOI: 10.3390/a10040118
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 119: An Optimization Algorithm Inspired by the
           Phase Transition Phenomenon for Global Optimization Problems with
           Continuous Variables

    • Authors: Zijian Cao, Lei Wang
      First page: 119
      Abstract: In this paper, we propose a novel nature-inspired meta-heuristic algorithm for continuous global optimization, named the phase transition-based optimization algorithm (PTBO). It mimics three completely different kinds of motion characteristics of elements in three different phases, which are the unstable phase, the meta-stable phase, and the stable phase. Three corresponding operators, which are the stochastic operator of the unstable phase, the shrinkage operator in the meta-stable phase, and the vibration operator of the stable phase, are designed in the proposed algorithm. In PTBO, the three different phases of elements dynamically execute different search tasks according to their phase in each generation. It makes it such that PTBO not only has a wide range of exploration capabilities, but also has the ability to quickly exploit them. Numerical experiments are carried out on twenty-eight functions of the CEC 2013 benchmark suite. The simulation results demonstrate its better performance compared with that of other state-of-the-art optimization algorithms.
      Citation: Algorithms
      PubDate: 2017-10-20
      DOI: 10.3390/a10040119
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 120: A Comparative Study on Recently-Introduced
           Nature-Based Global Optimization Methods in Complex Mechanical System
           Design

    • Authors: Abdulbaset Saad, Zuomin Dong, Meysam Karimi
      First page: 120
      Abstract: Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and “black-box” forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.
      Citation: Algorithms
      PubDate: 2017-10-17
      DOI: 10.3390/a10040120
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 121: Evolutionary Hybrid Particle Swarm
           Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling
           Problems

    • Authors: Laxmi Bewoor, V. Chandra Prakash, Sagar Sapkal
      First page: 121
      Abstract: The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO) metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO) algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH) heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard’s benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.
      Citation: Algorithms
      PubDate: 2017-10-28
      DOI: 10.3390/a10040121
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 122: Scheduling Non-Preemptible Jobs to
           Minimize Peak Demand

    • Authors: Sean Yaw, Brendan Mumey
      First page: 122
      Abstract: This paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown to be NP-hard. Our results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.
      Citation: Algorithms
      PubDate: 2017-10-28
      DOI: 10.3390/a10040122
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 123: A Selection Process for Genetic Algorithm
           Using Clustering Analysis

    • Authors: Adam Chehouri, Rafic Younes, Jihan Khoder, Jean Perron, Adrian Ilinca
      First page: 123
      Abstract: This article presents a newly proposed selection process for genetic algorithms on a class of unconstrained optimization problems. The k-means genetic algorithm selection process (KGA) is composed of four essential stages: clustering, membership phase, fitness scaling and selection. Inspired from the hypothesis that clustering the population helps to preserve a selection pressure throughout the evolution of the population, a membership probability index is assigned to each individual following the clustering phase. Fitness scaling converts the membership scores in a range suitable for the selection function which selects the parents of the next generation. Two versions of the KGA process are presented: using a fixed number of clusters K (KGAf) and via an optimal partitioning Kopt (KGAo) determined by two different internal validity indices. The performance of each method is tested on seven benchmark problems.
      Citation: Algorithms
      PubDate: 2017-11-02
      DOI: 10.3390/a10040123
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 124: Improvement of ID3 Algorithm Based on
           Simplified Information Entropy and Coordination Degree

    • Authors: Yingying Wang, Yibin Li, Yong Song, Xuewen Rong, Shuaishuai Zhang
      First page: 124
      Abstract: The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field of classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing multi-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed that combines the simplified information entropy based on different weights with coordination degree in rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using three common data samples as well as the decision tree classifiers. It is shown that the proposed algorithm has a better performance in the running time and tree structure, but not in accuracy than the ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large, the proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and accuracy. The experimental results show that the proposed algorithm is effective and viable.
      Citation: Algorithms
      PubDate: 2017-11-06
      DOI: 10.3390/a10040124
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 125: Simulation Optimization of Search and
           Rescue in Disaster Relief Based on Distributed Auction Mechanism

    • Authors: Jian Tang, Kejun Zhu, Haixiang Guo, Can Liao, Shuwen Zhang
      First page: 125
      Abstract: In this paper, we optimize the search and rescue (SAR) in disaster relief through agent-based simulation. We simulate rescue teams’ search behaviors with the improved Truncated Lévy walks. Then we propose a cooperative rescue plan based on a distributed auction mechanism, and illustrate it with the case of landslide disaster relief. The simulation is conducted in three scenarios, including “fatal”, “serious” and “normal”. Compared with the non-cooperative rescue plan, the proposed rescue plan in this paper would increase victims’ relative survival probability by 7–15%, increase the ratio of survivors getting rescued by 5.3–12.9%, and decrease the average elapsed time for one site getting rescued by 16.6–21.6%. The robustness analysis shows that search radius can affect the rescue efficiency significantly, while the scope of cooperation cannot. The sensitivity analysis shows that the two parameters, the time limit for completing rescue operations in one buried site and the maximum turning angle for next step, both have a great influence on rescue efficiency, and there exists optimal value for both of them in view of rescue efficiency.
      Citation: Algorithms
      PubDate: 2017-11-15
      DOI: 10.3390/a10040125
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 126: Linked Data for Life Sciences

    • Authors: Amrapali Zaveri, Gökhan Ertaylan
      First page: 126
      Abstract: Massive amounts of data are currently available and being produced at an unprecedented rate in all domains of life sciences worldwide. However, this data is disparately stored and is in different and unstructured formats making it very hard to integrate. In this review, we examine the state of the art and propose the use of the Linked Data (LD) paradigm, which is a set of best practices for publishing and connecting structured data on the Web in a semantically meaningful format. We argue that utilizing LD in the life sciences will make data sets better Findable, Accessible, Interoperable, and Reusable. We identify three tiers of the research cycle in life sciences, namely (i) systematic review of the existing body of knowledge, (ii) meta-analysis of data, and (iii) knowledge discovery of novel links across different evidence streams to primarily utilize the proposed LD paradigm. Finally, we demonstrate the use of LD in three use case scenarios along the same research question and discuss the future of data/knowledge integration in life sciences and the challenges ahead.
      Citation: Algorithms
      PubDate: 2017-11-16
      DOI: 10.3390/a10040126
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 127: A Real-Time Chinese Traffic Sign Detection
           Algorithm Based on Modified YOLOv2

    • Authors: Jianming Zhang, Manting Huang, Xiaokang Jin, Xudong Li
      First page: 127
      Abstract: Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.
      Citation: Algorithms
      PubDate: 2017-11-16
      DOI: 10.3390/a10040127
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 128: Truss Structure Optimization with Subset
           Simulation and Augmented Lagrangian Multiplier Method

    • Authors: Feng Du, Qiao-Yue Dong, Hong-Shuang Li
      First page: 128
      Abstract: This paper presents a global optimization method for structural design optimization, which integrates subset simulation optimization (SSO) and the dynamic augmented Lagrangian multiplier method (DALMM). The proposed method formulates the structural design optimization as a series of unconstrained optimization sub-problems using DALMM and makes use of SSO to find the global optimum. The combined strategy guarantees that the proposed method can automatically detect active constraints and provide global optimal solutions with finite penalty parameters. The accuracy and robustness of the proposed method are demonstrated by four classical truss sizing problems. The results are compared with those reported in the literature, and show a remarkable statistical performance based on 30 independent runs.
      Citation: Algorithms
      PubDate: 2017-11-21
      DOI: 10.3390/a10040128
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 129: Comparative Analysis of Classifiers for
           Classification of Emergency Braking of Road Motor Vehicles

    • Authors: Albert Podusenko, Vsevolod Nikulin, Ivan Tanev, Katsunori Shimohara
      First page: 129
      Abstract: We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of the dynamics of the accelerator pedal as the test set, the experimental results suggest that the evolved classifiers detect the emergency braking situation with at least 93% accuracy. The best performing classifier could be integrated into the agent that perceives the dynamics of the accelerator pedal in real time and—if emergency braking is detected—acts by applying full brakes well before the driver would have been able to apply them.
      Citation: Algorithms
      PubDate: 2017-11-22
      DOI: 10.3390/a10040129
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 130: 2-Phase NSGA II: An Optimized Reward and
           Risk Measurements Algorithm in Portfolio Optimization

    • Authors: Seyedeh Eftekharian, Mohammad Shojafar, Shahaboddin Shamshirband
      First page: 130
      Abstract: Portfolio optimization is a serious challenge for financial engineering and has pulled down special attention among investors. It has two objectives: to maximize the reward that is calculated by expected return and to minimize the risk. Variance has been considered as a risk measure. There are many constraints in the world that ultimately lead to a non–convex search space such as cardinality constraint. In conclusion, parametric quadratic programming could not be applied and it seems essential to apply multi-objective evolutionary algorithm (MOEA). In this paper, a new efficient multi-objective portfolio optimization algorithm called 2-phase NSGA II algorithm is developed and the results of this algorithm are compared with the NSGA II algorithm. It was found that 2-phase NSGA II significantly outperformed NSGA II algorithm.
      Citation: Algorithms
      PubDate: 2017-11-28
      DOI: 10.3390/a10040130
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 131: An Indoor Collaborative
           Coefficient-Triangle APIT Localization Algorithm

    • Authors: Su-Ting Chen, Chuang Zhang, Peng Li, Yan-Yan Zhang, Liang-Bao Jiao
      First page: 131
      Abstract: The Approximate Point-In-Triangulation (APIT) localization algorithm is a widely used indoor positioning technology due to its simplicity and low power consumption. However, in practice, In-to-Out misjudgments exist regularly in APIT, and a considerable amount of nodes cannot be positioned due to the low node density. To tackle this issue, a Collaborative Coefficient-triangle APIT Localization (CCAL) algorithm is proposed. Firstly, an effective triangle criterion is put forward to reduce the probability of In-to-Out misjudgment and reduce the computational complexity. Then, a further Received Signal Strength Indicator (RSSI) location and weighted triangle coordinate calculation method is adopted to reduce the positioning error. Meanwhile, the idea of iterative collaborative positioning of the positioned unknown nodes is introduced to remarkably expand the localization coverage rate. Simulation results show that the proposed algorithm outperforms APIT, RSSI, and other improved algorithms in terms of both node location error and localization coverage rate.
      Citation: Algorithms
      PubDate: 2017-11-28
      DOI: 10.3390/a10040131
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 132: Hyperspectral Data: Efficient and Secure
           Transmission

    • Authors: Raffaele Pizzolante, Bruno Carpentieri
      First page: 132
      Abstract: Airborne and spaceborne hyperspectral sensors collect information which is derived from the electromagnetic spectrum of an observed area. Hyperspectral data are used in several studies and they are an important aid in different real-life applications (e.g., mining and geology applications, ecology, surveillance, etc.). A hyperspectral image has a three-dimensional structure (a sort of datacube): it can be considered as a sequence of narrow and contiguous spectral channels (bands). The objective of this paper is to present a framework permits the efficient storage/transmission of an input hyperspectral image, and its protection. The proposed framework relies on a reversible invisible watermarking scheme and an efficient lossless compression algorithm. The reversible watermarking scheme is used in conjunction with digital signature techniques in order to permit the verification of the integrity of a hyperspectral image by the receiver.
      Citation: Algorithms
      PubDate: 2017-11-30
      DOI: 10.3390/a10040132
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 133: Neutrosophic Linear Equations and
           Application in Traffic Flow Problems

    • Authors: Jun Ye
      First page: 133
      Abstract: A neutrosophic number (NN) presented by Smarandache can express determinate and/or indeterminate information in real life. NN (z = a + uI) consists of the determinate part a and the indeterminate part uI for a, u ∈ R (R is all real numbers) and indeterminacy I, and is very suitable for representing and handling problems with both determinate and indeterminate information. Based on the concept of NNs, this paper presents for first time the concepts of neutrosophic linear equations and the neutrosophic matrix, and introduces the neutrosophic matrix operations. Then, we propose some solving methods, including the substitution method, the addition method, and the inverse matrix method, for the system of neutrosophic linear equations or the neutrosophic matrix equation. Finally, an applied example about a traffic flow problem is provided to illustrate the application and effectiveness of handling the indeterminate traffic flow problem by using the system of neutrosophic linear equations.
      Citation: Algorithms
      PubDate: 2017-12-01
      DOI: 10.3390/a10040133
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 134: Improved Integral Inequalities for
           Stability Analysis of Interval Time-Delay Systems

    • Authors: Shuai Zhang, Xiao Qi
      First page: 134
      Abstract: A novel stability analysis for the interval time-delay systems is proposed by employing a new series of integral inequalities for single and double integrals. Different from the recently introduced Wirtinger-based inequalities, refined Jensen inequalities and auxiliary function-based inequalities, the proposed ones can provide more accurate bounds for the cross terms in derivatives of the Lyapunov–Krasovskii functional (LKF) without involving additional slack variables. Based on the augmented LKF with triple-integral terms, their applications to stability analysis for interval time-delay systems are provided. By virtue of the newly derived inequalities, the resulting criteria are less conservative than some existing literature. Finally, numerical examples are provided to verify the effectiveness and improvement of the proposed approaches.
      Citation: Algorithms
      PubDate: 2017-12-03
      DOI: 10.3390/a10040134
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 135: Algebraic Dynamic Programming on Trees

    • Authors: Sarah Berkemer, Christian Höner zu Siederdissen, Peter Stadler
      First page: 135
      Abstract: Where string grammars describe how to generate and parse strings, tree grammars describe how to generate and parse trees. We show how to extend generalized algebraic dynamic programming to tree grammars. The resulting dynamic programming algorithms are efficient and provide the complete feature set available to string grammars, including automatic generation of outside parsers and algebra products for efficient backtracking. The complete parsing infrastructure is available as an embedded domain-specific language in Haskell. In addition to the formal framework, we provide implementations for both tree alignment and tree editing. Both algorithms are in active use in, among others, the area of bioinformatics, where optimization problems on trees are of considerable practical importance. This framework and the accompanying algorithms provide a beneficial starting point for developing complex grammars with tree- and forest-based inputs.
      Citation: Algorithms
      PubDate: 2017-12-06
      DOI: 10.3390/a10040135
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 136: Detecting Composite Functional Module in
           miRNA Regulation and mRNA Interaction Network

    • Authors: Yi Yang, Chu Pan
      First page: 136
      Abstract: The detection of composite miRNA functional module (CMFM) is of tremendous significance and helps in understanding the organization, regulation and execution of cell processes in cancer, but how to identify functional CMFMs is still a computational challenge. In this paper we propose a novel module detection method called MBCFM (detecting Composite Function Modules based on Maximal Biclique enumeration), specifically designed to bicluster miRNAs and target messenger RNAs (mRNAs) on the basis of multiple biological interaction information and topical network features. In this method, we employ algorithm MICA to enumerate all maximal bicliques and further extract R-pairs from the miRNA-mRNA regulatory network. Compared with two existing methods, Mirsynergy and SNMNMF on ovarian cancer dataset, the proposed method of MBCFM is not only able to extract cohesiveness-preserved CMFMs but also has high efficiency in running time. More importantly, MBCFM can be applied to detect other cancer-associated miRNA functional modules.
      Citation: Algorithms
      PubDate: 2017-12-05
      DOI: 10.3390/a10040136
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 137: Weakly Coupled Distributed Calculation of
           Lyapunov Exponents for Non-Linear Dynamical Systems

    • Authors: Jorge Hernández-Gómez, Carlos Couder-Castañeda, Israel Herrera-Díaz, Norberto Flores-Guzmán, Enrique Gómez-Cruz
      First page: 137
      Abstract: Numerical estimation of Lyapunov exponents in non-linear dynamical systems results in a very high computational cost. This is due to the large-scale computational cost of several Runge–Kutta problems that need to be calculated. In this work we introduce a parallel implementation based on MPI (Message Passing Interface) for the calculation of the Lyapunov exponents for a multidimensional dynamical system, considering a weakly coupled algorithm. Since we work on an academic high-latency cluster interconnected with a gigabit switch, the design has to be oriented to reduce the number of messages required. With the design introduced in this work, the computing time is drastically reduced, and the obtained performance leads to close to optimal speed-up ratios. The implemented parallelisation allows us to carry out many experiments for the calculation of several Lyapunov exponents with a low-cost cluster. The numerical experiments showed a high scalability, which we showed with up to 68 cores.
      Citation: Algorithms
      PubDate: 2017-12-07
      DOI: 10.3390/a10040137
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 138: A Hierarchical Multi-Label Classification
           Algorithm for Gene Function Prediction

    • Authors: Shou Feng, Ping Fu, Wenbin Zheng
      First page: 138
      Abstract: Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms.
      Citation: Algorithms
      PubDate: 2017-12-08
      DOI: 10.3390/a10040138
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 139: An EMD–SARIMA-Based Modeling Approach
           for Air Traffic Forecasting

    • Authors: Wei Nai, Lu Liu, Shaoyin Wang, Decun Dong
      First page: 139
      Abstract: The ever-increasing air traffic demand in China has brought huge pressure on the planning and management of, and investment in, air terminals as well as airline companies. In this context, accurate and adequate short-term air traffic forecasting is essential for the operations of those entities. In consideration of such a problem, a hybrid air traffic forecasting model based on empirical mode decomposition (EMD) and seasonal auto regressive integrated moving average (SARIMA) has been proposed in this paper. The model proposed decomposes the original time series into components at first, and models each component with the SARIMA forecasting model, then integrates all the models together to form the final combined forecast result. By using the monthly air cargo and passenger flow data from the years 2006 to 2014 available at the official website of the Civil Aviation Administration of China (CAAC), the effectiveness in forecasting of the model proposed has been demonstrated, and by a horizontal performance comparison between several other widely used forecasting models, the advantage of the proposed model has also been proved.
      Citation: Algorithms
      PubDate: 2017-12-14
      DOI: 10.3390/a10040139
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 140: Control-Oriented Models for SO Fuel Cells
           from the Angle of V&V: Analysis, Simplification Possibilities,
           Performance

    • Authors: Ekaterina Auer, Luise Senkel, Stefan Kiel, Andreas Rauh
      First page: 140
      Abstract: In this paper, we take a look at the analysis and parameter identification for control-oriented, dynamic models for the thermal subsystem of solid oxide fuel cells (SOFC) from the systematized point of view of verification and validation (V&V). First, we give a possible classification of models according to their verification degree which depends, for example, on the kind of arithmetic used for both formulation and simulation. Typical SOFC models, consisting of several coupled differential equations for gas preheaters and the temperature distribution in the stack module, do not have analytical solutions because of spatial nonlinearity. Therefore, in the next part of the paper, we describe in detail two possible ways to simplify such models so that the underlying differential equations can be solved analytically while still being sufficiently accurate to serve as the basis for control synthesis. The simplifying assumption is to approximate the heat capacities of the gases by zero-order polynomials (or first-oder polynomials, respectively) in the temperature. In the last, application-oriented part of the paper, we identify the parameters of these models as well as compare their performance and their ability to reflect the reality with the corresponding characteristics of models in which the heat capacities are represented by quadratic polynomials (the usual case). For this purpose, the framework UniVerMeC (Unified Framework for Verified GeoMetric Computations) is used, which allows us to employ different kinds of arithmetics including the interval one. This latter possibility ensures a high level of reliability of simulations and of the subsequent validation. Besides, it helps to take into account bounded uncertainty in measurements.
      Citation: Algorithms
      PubDate: 2017-12-18
      DOI: 10.3390/a10040140
      Issue No: Vol. 10, No. 4 (2017)
       
  • Algorithms, Vol. 10, Pages 88: On the Lagged Diffusivity Method for the
           Solution of Nonlinear Finite Difference Systems

    • Authors: Francesco Mezzadri, Emanuele Galligani
      First page: 88
      Abstract: In this paper, we extend the analysis of the Lagged Diffusivity Method for nonlinear, non-steady reaction-convection-diffusion equations. In particular, we describe how the method can be used to solve the systems arising from different discretization schemes, recalling some results on the convergence of the method itself. Moreover, we also analyze the behavior of the method in case of problems presenting boundary layers or blow-up solutions.
      Citation: Algorithms
      PubDate: 2017-08-02
      DOI: 10.3390/a10030088
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 90: Local Community Detection Based on Small
           Cliques

    • Authors: Michael Hamann, Eike Röhrs, Dorothea Wagner
      First page: 90
      Abstract: Community detection aims to find dense subgraphs in a network. We consider the problem of finding a community locally around a seed node both in unweighted and weighted networks. This is a faster alternative to algorithms that detect communities that cover the whole network when actually only a single community is required. Further, many overlapping community detection algorithms use local community detection algorithms as basic building block. We provide a broad comparison of different existing strategies of expanding a seed node greedily into a community. For this, we conduct an extensive experimental evaluation both on synthetic benchmark graphs as well as real world networks. We show that results both on synthetic as well as real-world networks can be significantly improved by starting from the largest clique in the neighborhood of the seed node. Further, our experiments indicate that algorithms using scores based on triangles outperform other algorithms in most cases. We provide theoretical descriptions as well as open source implementations of all algorithms used.
      Citation: Algorithms
      PubDate: 2017-08-11
      DOI: 10.3390/a10030090
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 91: Transformation-Based Fuzzy Rule
           Interpolation Using Interval Type-2 Fuzzy Sets

    • Authors: Chengyuan Chen, Qiang Shen
      First page: 91
      Abstract: In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both interpolation and extrapolation with multiple multi-antecedent rules. However, the difficult problem of defining the precise-valued membership functions required in the representation of fuzzy rules, or of the observations, restricts its applications. Fortunately, this problem can be alleviated through the use of type-2 fuzzy sets, owing to the fact that the membership functions of such fuzzy sets are themselves fuzzy, providing a more flexible means of modelling. This paper therefore, extends the existing T-FRI approach using interval type-2 fuzzy sets, which covers the original T-FRI as its specific instance. The effectiveness of this extension is demonstrated by experimental investigations and, also, by a practical application in comparison to the state-of-the-art alternative approach developed using rough-fuzzy sets.
      Citation: Algorithms
      PubDate: 2017-08-15
      DOI: 10.3390/a10030091
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 92: Automatic Modulation Recognition Using
           Compressive Cyclic Features

    • Authors: Lijin Xie, Qun Wan
      First page: 92
      Abstract: Higher-order cyclic cumulants (CCs) have been widely adopted for automatic modulation recognition (AMR) in cognitive radio. However, the CC-based AMR suffers greatly from the requirement of high-rate sampling. To overcome this limit, we resort to the theory of compressive sensing (CS). By exploiting the sparsity of CCs, recognition features can be extracted from a small amount of compressive measurements via a rough CS reconstruction algorithm. Accordingly, a CS-based AMR scheme is formulated. Simulation results demonstrate the availability and robustness of the proposed approach.
      Citation: Algorithms
      PubDate: 2017-08-18
      DOI: 10.3390/a10030092
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 93: Post-Processing Partitions to Identify
           Domains of Modularity Optimization

    • Authors: William Weir, Scott Emmons, Ryan Gibson, Dane Taylor, Peter Mucha
      First page: 93
      Abstract: We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP) algorithm to prune and prioritize different network community structures identified across multiple runs of possibly various computational heuristics. Given a set of partitions, CHAMP identifies the domain of modularity optimization for each partition—i.e., the parameter-space domain where it has the largest modularity relative to the input set—discarding partitions with empty domains to obtain the subset of partitions that are “admissible” candidate community structures that remain potentially optimal over indicated parameter domains. Importantly, CHAMP can be used for multi-dimensional parameter spaces, such as those for multilayer networks where one includes a resolution parameter and interlayer coupling. Using the results from CHAMP, a user can more appropriately select robust community structures by observing the sizes of domains of optimization and the pairwise comparisons between partitions in the admissible subset. We demonstrate the utility of CHAMP with several example networks. In these examples, CHAMP focuses attention onto pruned subsets of admissible partitions that are 20-to-1785 times smaller than the sets of unique partitions obtained by community detection heuristics that were input into CHAMP.
      Citation: Algorithms
      PubDate: 2017-08-19
      DOI: 10.3390/a10030093
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 94: NBTI and Power Reduction Using an Input
           Vector Control and Supply Voltage Assignment Method

    • Authors: Peng Sun, Zhiming Yang, Yang Yu, Junbao Li, Xiyuan Peng
      First page: 94
      Abstract: As technology scales, negative bias temperature instability (NBTI) becomes one of the primary failure mechanisms for Very Large Scale Integration (VLSI) circuits. Meanwhile, the leakage power increases dramatically as the supply/threshold voltage continues to scale down. These two issues pose severe reliability problems for complementary metal oxide semiconductor (CMOS) devices. Because both the NBTI and leakage are dependent on the input vector of the circuit, we present an input vector control (IVC) method based on a linear programming algorithm, which can co-optimize circuit aging and power dissipation simultaneously. In addition, our proposed IVC method is combined with the supply voltage assignment technique to further reduce delay degradation and leakage power. Experimental results on various circuits show the effectiveness of the proposed combination method.
      Citation: Algorithms
      PubDate: 2017-08-19
      DOI: 10.3390/a10030094
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 95: A Parallel Two-Stage Iteration Method for
           Solving Continuous Sylvester Equations

    • Authors: Manyu Xiao, Quanyi Lv, Zhuo Xing, Yingchun Zhang
      First page: 95
      Abstract: In this paper we propose a parallel two-stage iteration algorithm for solving large-scale continuous Sylvester equations. By splitting the coefficient matrices, the original linear system is transformed into a symmetric linear system which is then solved by using the SYMMLQ algorithm. In order to improve the relative parallel efficiency, an adjusting strategy is explored during the iteration calculation of the SYMMLQ algorithm to decrease the degree of the reduce-operator from two to one communications at each step. Moreover, the convergence of the iteration scheme is discussed, and finally numerical results are reported showing that the proposed method is an efficient and robust algorithm for this class of continuous Sylvester equations on a parallel machine.
      Citation: Algorithms
      PubDate: 2017-08-21
      DOI: 10.3390/a10030095
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 96: Double-Threshold Cooperative Spectrum
           Sensing Algorithm Based on Sevcik Fractal Dimension

    • Authors: Xueying Diao, Qianhui Dong, Zijian Yang, Yibing Li
      First page: 96
      Abstract: Spectrum sensing is of great importance in the cognitive radio (CR) networks. Compared with individual spectrum sensing, cooperative spectrum sensing (CSS) has been shown to greatly improve the accuracy of the detection. However, the existing CSS algorithms are sensitive to noise uncertainty and are inaccurate in low signal-to-noise ratio (SNR) detection. To address this, we propose a double-threshold CSS algorithm based on Sevcik fractal dimension (SFD) in this paper. The main idea of the presented scheme is to sense the presence of primary users in the local spectrum sensing by analyzing different characteristics of the SFD between signals and noise. Considering the stochastic fluctuation characteristic of the noise SFD in a certain range, we adopt the double-threshold method in the multi-cognitive user CSS so as to improve the detection accuracy, where thresholds are set according to the maximum and minimum values of the noise SFD. After obtaining the detection results, the cognitive user sends local detection results to the fusion center for reliability fusion. Simulation results demonstrate that the proposed method is insensitive to noise uncertainty. Simulations also show that the algorithm presented in this paper can achieve high detection performance at the low SNR region.
      Citation: Algorithms
      PubDate: 2017-08-21
      DOI: 10.3390/a10030096
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 97: A Simplified Matrix Formulation for
           Sensitivity Analysis of Hidden Markov Models

    • Authors: Seifemichael B. Amsalu,  Abdollah Homaifar, Albert Esterline
      First page: 97
      Abstract: In this paper, a new algorithm for sensitivity analysis of discrete hidden Markov models (HMMs) is proposed. Sensitivity analysis is a general technique for investigating the robustness of the output of a system model. Sensitivity analysis of probabilistic networks has recently been studied extensively. This has resulted in the development of mathematical relations between a parameter and an output probability of interest and also methods for establishing the effects of parameter variations on decisions. Sensitivity analysis in HMMs has usually been performed by taking small perturbations in parameter values and re-computing the output probability of interest. As recent studies show, the sensitivity analysis of an HMM can be performed using a functional relationship that describes how an output probability varies as the network’s parameters of interest change. To derive this sensitivity function, existing Bayesian network algorithms have been employed for HMMs. These algorithms are computationally inefficient as the length of the observation sequence and the number of parameters increases. In this study, a simplified efficient matrix-based algorithm for computing the coefficients of the sensitivity function for all hidden states and all time steps is proposed and an example is presented.
      Citation: Algorithms
      PubDate: 2017-08-22
      DOI: 10.3390/a10030097
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 98: Adaptive Virtual RSU Scheduling for
           Scalable Coverage under Bidirectional Vehicle Traffic Flow

    • Authors: Fei Chen, Xiaohong Bi, Ruimin Lyu, Zhongwei Hua, Yuan Liu, Xiaoting Zhang
      First page: 98
      Abstract: Over the past decades, vehicular ad hoc networks (VANETs) have been a core networking technology to provide drivers and passengers with safety and convenience. As a new emerging technology, the vehicular cloud computing (VCC) can provide cloud services for various data-intensive applications in VANETs, such as multimedia streaming. However, the vehicle mobility and intermittent connectivity present challenges to the large-scale data dissemination with underlying computing and networking architecture. In this paper, we will explore the service scheduling of virtual RSUs for diverse request demands in the dynamic traffic flow in vehicular cloud environment. Specifically, we formulate the RSU allocation problem as maximum service capacity with multiple-source and multiple-destination, and propose a bidirectional RSU allocation strategy. In addition, we formulate the content replication in distributed RSUs as the minimum replication set coverage problem in a two-layer mapping model, and analyze the solutions in different scenarios. Numerical results further prove the superiority of our proposed solution, as well as the scalability to various traffic condition variations.
      Citation: Algorithms
      PubDate: 2017-08-24
      DOI: 10.3390/a10030098
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 99: Hybrid Learning for General Type-2 TSK
           Fuzzy Logic Systems

    • Authors: Mauricio Sanchez, Juan Castro, Violeta Ocegueda-Miramontes, Leticia Cervantes
      First page: 99
      Abstract: This work is focused on creating fuzzy granular classification models based on general type-2 fuzzy logic systems when consequents are represented by interval type-2 TSK linear functions. Due to the complexity of general type-2 TSK fuzzy logic systems, a hybrid learning approach is proposed, where the principle of justifiable granularity is heuristically used to define an amount of uncertainty in the system, which in turn is used to define the parameters in the interval type-2 TSK linear functions via a dual LSE algorithm. Multiple classification benchmark datasets were tested in order to assess the quality of the formed granular models; its performance is also compared against other common classification algorithms. Shown results conclude that classification performance in general is better than results obtained by other techniques, and in general, all achieved results, when averaged, have a better performance rate than compared techniques, demonstrating the stability of the proposed hybrid learning technique.
      Citation: Algorithms
      PubDate: 2017-08-25
      DOI: 10.3390/a10030099
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 100: Biogeography-Based Optimization of the
           Portfolio Optimization Problem with Second Order Stochastic Dominance
           Constraints

    • Authors: Tao Ye, Ziqiang Yang, Siling Feng
      First page: 100
      Abstract: The portfolio optimization problem is the central problem of modern economics and decision theory; there is the Mean-Variance Model and Stochastic Dominance Model for solving this problem. In this paper, based on the second order stochastic dominance constraints, we propose the improved biogeography-based optimization algorithm to optimize the portfolio, which we called ε BBO. In order to test the computing power of ε BBO, we carry out two numerical experiments in several kinds of constraints. In experiment 1, comparing the Stochastic Approximation (SA) method with the Level Function (LF) algorithm and Genetic Algorithm (GA), we get a similar optimal solution by ε BBO in [ 0 , 0 . 6 ] and [ 0 , 1 ] constraints with the return of 1.174% and 1.178%. In [ - 1 , 2 ] constraint, we get the optimal return of 1.3043% by ε BBO, while the return of SA and LF is 1.23% and 1.26%. In experiment 2, we get the optimal return of 0.1325% and 0.3197% by ε BBO in [ 0 , 0 . 1 ] and [ - 0 . 05 , 0 . 15 ] constraints. As a comparison, the return of FTSE100 Index portfolio is 0.0937%. The results prove that ε BBO algorithm has great potential in the field of financial decision-making, it also shows that ε BBO algorithm has a better performance in optimization problem.
      Citation: Algorithms
      PubDate: 2017-08-25
      DOI: 10.3390/a10030100
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 101: Comparative Study of Type-2 Fuzzy Particle
           

    • Authors: Frumen Olivas, Leticia Amador-Angulo, Jonathan Perez, Camilo Caraveo, Fevrier Valdez, Oscar Castillo
      First page: 101
      Abstract: In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithms (original and modified versions) are compared with the design of fuzzy systems used for controlling the trajectory of an autonomous mobile robot. Simulation results reveal that PSO algorithm outperforms the results of the BCO and BA algorithms.
      Citation: Algorithms
      PubDate: 2017-08-28
      DOI: 10.3390/a10030101
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 102: Local Community Detection in Dynamic
           Graphs Using Personalized Centrality

    • Authors: Eisha Nathan, Anita Zakrzewska, Jason Riedy, David Bader
      First page: 102
      Abstract: Analyzing massive graphs poses challenges due to the vast amount of data available. Extracting smaller relevant subgraphs allows for further visualization and analysis that would otherwise be too computationally intensive. Furthermore, many real data sets are constantly changing, and require algorithms to update as the graph evolves. This work addresses the topic of local community detection, or seed set expansion, using personalized centrality measures, specifically PageRank and Katz centrality. We present a method to efficiently update local communities in dynamic graphs. By updating the personalized ranking vectors, we can incrementally update the corresponding local community. Applying our methods to real-world graphs, we are able to obtain speedups of up to 60× compared to static recomputation while maintaining an average recall of 0.94 of the highly ranked vertices returned. Next, we investigate how approximations of a centrality vector affect the resulting local community. Specifically, our method guarantees that the vertices returned in the community are the highly ranked vertices from a personalized centrality metric.
      Citation: Algorithms
      PubDate: 2017-08-29
      DOI: 10.3390/a10030102
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 103: An Enhanced Dynamic Spectrum Allocation
           Algorithm Based on Cournot Game in Maritime Cognitive Radio Communication
           System

    • Authors: Jingbo Zhang, Henan Yu, Shufang Zhang
      First page: 103
      Abstract: The recent development of maritime transport has resulted in the demand for a wider communication bandwidth being more intense. Cognitive radios can dynamically manage resources in a spectrum. Thus, building a new type of maritime cognitive radio communication system (MCRCS) is an effective solution. In this paper, the enhanced dynamic spectrum allocation algorithm (EDSAA) is proposed, which is based on the Cournot game model. In EDSAA, the decision-making center (DC) sets the weights according to the detection capability of the secondary user (SU), before adding these weighting coefficients in the price function. Furthermore, the willingness of the SU will reduce after meeting their basic communication needs when it continues to increase the leasable spectrum by adding the elastic model in the SU’s revenue function. On this basis, the profit function is established. The simulation results show that the EDSAA has Nash equilibrium and conforms to the actual situation. It shows that the results of spectrum allocation are fair, efficient and reasonable.
      Citation: Algorithms
      PubDate: 2017-09-03
      DOI: 10.3390/a10030103
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 104: Contract-Based Incentive Mechanism for
           Mobile Crowdsourcing Networks

    • Authors: Nan Zhao, Menglin Fan, Chao Tian, Pengfei Fan
      First page: 104
      Abstract: Mobile crowdsourcing networks (MCNs) are a promising method of data collecting and processing by leveraging the mobile devices’ sensing and computing capabilities. However, because of the selfish characteristics of the service provider (SP) and mobile users (MUs), crowdsourcing participants only aim to maximize their own benefits. This paper investigates the incentive mechanism between the above two parties to create mutual benefits. By modeling MCNs as a labor market, a contract-based crowdsourcing model with moral hazard is proposed under the asymmetric information scenario. In order to incentivize the potential MUs to participate in crowdsourcing tasks, the optimization problem is formulated to maximize the SP’s utility by jointly examining the crowdsourcing participants’ risk preferences. The impact of crowdsourcing participants’ attitudes of risks on the incentive mechanism has been studied analytically and experimentally. Numerical simulation results demonstrate the effectiveness of the proposed contract design scheme for the crowdsourcing incentive.
      Citation: Algorithms
      PubDate: 2017-09-04
      DOI: 10.3390/a10030104
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 105: Comparison of Internal Clustering
           Validation Indices for Prototype-Based Clustering

    • Authors: Joonas Hämäläinen, Susanne Jauhiainen, Tommi Kärkkäinen
      First page: 105
      Abstract: Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on the behavior of different variants of clustering algorithms will be given.
      Citation: Algorithms
      PubDate: 2017-09-06
      DOI: 10.3390/a10030105
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 106: Type-1 Fuzzy Sets and Intuitionistic Fuzzy
           Sets

    • Authors: Krassimir Atanassov
      First page: 106
      Abstract: A comparison between type-1 fuzzy sets (T1FSs) and intuitionistic fuzzy sets (IFSs) is made. The operators defined over IFSs that do not have analogues in T1FSs are shown, and such analogues are introduced whenever possible.
      Citation: Algorithms
      PubDate: 2017-09-13
      DOI: 10.3390/a10030106
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 107: A Monarch Butterfly Optimization for the
           Dynamic Vehicle Routing Problem

    • Authors: Shifeng Chen, Rong Chen, Jian Gao
      First page: 107
      Abstract: The dynamic vehicle routing problem (DVRP) is a variant of the Vehicle Routing Problem (VRP) in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO) algorithm to solve DVRPs, utilizing a greedy strategy. Both migration operation and the butterfly adjusting operator only accept the offspring of butterfly individuals that have better fitness than their parents. To improve performance, a later perturbation procedure is implemented, to maintain a balance between global diversification and local intensification. The computational results indicate that the proposed technique outperforms the existing approaches in the literature for average performance by at least 9.38%. In addition, 12 new best solutions were found. This shows that this proposed technique consistently produces high-quality solutions and outperforms other published heuristics for the DVRP.
      Citation: Algorithms
      PubDate: 2017-09-12
      DOI: 10.3390/a10030107
      Issue No: Vol. 10, No. 3 (2017)
       
  • Algorithms, Vol. 10, Pages 108: Performance Analysis of Four
           Decomposition-Ensemble Models for One-Day-Ahead Agricultural Commodity
           Futures Price Forecasting

    • Authors: Deyun Wang, Chenqiang Yue, Shuai Wei, Jun Lv
      First page: 108
      Abstract: Agricultural commodity futures prices play a significant role in the change tendency of these spot prices and the supply–demand relationship of global agricultural product markets. Due to the nonlinear and nonstationary nature of this kind of time series data, it is inevitable for price forecasting research to take this nature into consideration. Therefore, we aim to enrich the existing research literature and offer a new way of thinking about forecasting agricultural commodity futures prices, so that four hybrid models are proposed based on the back propagation neural network (BPNN) optimized by the particle swarm optimization (PSO) algorithm and four decomposition methods: empirical mode decomposition (EMD), wavelet packet transform (WPT), intrinsic time-scale decomposition (ITD) and variational mode decomposition (VMD). In order to verify the applicability and validity of these hybrid models, we select three futures prices of wheat, corn and soybean to conduct the experiment. The experimental results show that (1) all the hybrid models combined with decomposition technique have a better performance than the single PSO–BPNN model; (2) VMD contributes the most in improving the forecasting ability of the PSO–BPNN model, while WPT ranks second; (3) ITD performs better than EMD in both cases of corn and soybean; and (4) the proposed models perform well in the forecasting of agricultural commodity futures prices.
      Citation: Algorithms
      PubDate: 2017-09-12
      DOI: 10.3390/a10030108
      Issue No: Vol. 10, No. 3 (2017)
       
 
 
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