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MATHEMATICS (711 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 4)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 7)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 29)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19)
Advances in Decision Sciences     Open Access   (Followers: 3)
Advances in Difference Equations     Open Access   (Followers: 3)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 13)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 3)
Advances in Materials Sciences     Open Access   (Followers: 14)
Advances in Mathematical Physics     Open Access   (Followers: 4)
Advances in Mathematics     Full-text available via subscription   (Followers: 11)
Advances in Numerical Analysis     Open Access   (Followers: 5)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 6)
Advances in Pure Mathematics     Open Access   (Followers: 6)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 5)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 11)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 5)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Full-text available via subscription   (Followers: 5)
Algorithms     Open Access   (Followers: 11)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 6)
American Journal of Operations Research     Open Access   (Followers: 5)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 8)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access   (Followers: 1)
Analysis     Hybrid Journal   (Followers: 2)
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5)
Analysis Mathematica     Full-text available via subscription  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales UMCS, Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics     Full-text available via subscription   (Followers: 1)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access  
Annuaire du Collège de France     Open Access   (Followers: 5)
ANZIAM Journal     Open Access   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 2)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 7)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 6)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 2)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 3)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 2)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Armenian Journal of Mathematics     Open Access  
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites : The Journal of Space Research Centre of Polish Academy of Sciences     Open Access   (Followers: 20)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Journal of Current Engineering & Maths     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 6)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 4)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access   (Followers: 1)
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 1)
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access   (Followers: 2)
BIT Numerical Mathematics     Hybrid Journal  
BoEM - Boletim online de Educação Matemática     Open Access  
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription   (Followers: 1)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 20)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 12)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 4)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 1)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of Symbolic Logic     Full-text available via subscription   (Followers: 2)
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 1)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 4)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 18)
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Clean Air Journal     Full-text available via subscription   (Followers: 1)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Collectanea Mathematica     Hybrid Journal  
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 2)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 3)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Compositio Mathematica     Full-text available via subscription   (Followers: 1)
Comptes Rendus Mathematique     Full-text available via subscription   (Followers: 1)
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 Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 8)
Concrete Operators     Open Access   (Followers: 5)
Confluentes Mathematici     Hybrid Journal  
Contributions to Game Theory and Management     Open Access  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 13)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 9)
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 11)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 29)
Developments in Clay Science     Full-text available via subscription   (Followers: 1)
Developments in Mineral Processing     Full-text available via subscription   (Followers: 3)
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 3)
Differentsial'nye Uravneniya     Open Access  
Discrete Mathematics     Hybrid Journal   (Followers: 8)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae - General Algebra and Applications     Open Access  
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Diskretnaya Matematika     Full-text available via subscription  
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Akademii Nauk     Open Access  
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Eco Matemático     Open Access  
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Combinatorics     Open Access  
Electronic Journal of Differential Equations     Open Access  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 2)
Electronic Notes in Discrete Mathematics     Full-text available via subscription   (Followers: 2)
Elemente der Mathematik     Full-text available via subscription   (Followers: 4)
Energy for Sustainable Development     Hybrid Journal   (Followers: 9)

        1 2 3 4 | Last

Journal Cover
Journal Prestige (SJR): 0.217
Citation Impact (citeScore): 1
Number of Followers: 11  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1999-4893
Published by MDPI Homepage  [202 journals]
  • Algorithms, Vol. 11, Pages 78: A Modified Artificial Bee Colony Algorithm
           Based on the Self-Learning Mechanism

    • Authors: Bao Pang, Yong Song, Chengjin Zhang, Hongling Wang, Runtao Yang
      First page: 78
      Abstract: Artificial bee colony (ABC) algorithm, a novel category of bionic intelligent optimization algorithm, was achieved for solving complex nonlinear optimization problems. Previous studies have shown that ABC algorithm is competitive to other biological-inspired optimization algorithms, but there still exist several insufficiencies due to the inefficient solution search equation (SSE), which does well in exploration but poorly in exploitation. To improve accuracy of the solutions, this paper proposes a modified ABC algorithm based on the self-learning mechanism (SLABC) with five SSEs as the candidate operator pool; among them, one is good at exploration and two of them are good at exploitation; another SSE intends to balance exploration and exploitation; moreover, the last SSE with Lévy flight step-size which can generate smaller step-size with high frequency and bigger step-size occasionally not only can balance exploration and exploitation but also possesses the ability to escape from the local optimum. This paper proposes a simple self-learning mechanism, wherein the SSE is selected according to the previous success ratio in generating promising solutions at each iteration. Experiments on a set of 9 benchmark functions are carried out with the purpose of evaluating the performance of the proposed method. The experimental results illustrated that the SLABC algorithm achieves significant improvement compared with other competitive algorithms.
      Citation: Algorithms
      PubDate: 2018-05-24
      DOI: 10.3390/a11060078
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 79: A Fire Detection Algorithm Based on
           Tchebichef Moment Invariants and PSO-SVM

    • Authors: Yongming Bian, Meng Yang, Xuying Fan, Yuchao Liu
      First page: 79
      Abstract: Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS.
      Citation: Algorithms
      PubDate: 2018-05-25
      DOI: 10.3390/a11060079
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 80: Scheduling a Single Machine with Primary
           and Secondary Objectives

    • Authors: Nodari Vakhania
      First page: 80
      Abstract: We study a scheduling problem in which jobs with release times and due dates are to be processed on a single machine. With the primary objective to minimize the maximum job lateness, the problem is strongly NP-hard. We describe a general algorithmic scheme to minimize the maximum job lateness, with the secondary objective to minimize the maximum job completion time. The problem of finding the Pareto-optimal set of feasible solutions with these two objective criteria is strongly NP-hard. We give the dominance properties and conditions when the Pareto-optimal set can be formed in polynomial time. These properties, together with our general framework, provide the theoretical background, so that the basic framework can be expanded to (exponential-time) implicit enumeration algorithms and polynomial-time approximation algorithms (generating the Pareto sub-optimal frontier with a fair balance between the two objectives). Some available in the literature experimental results confirm the practical efficiency of the proposed framework.
      Citation: Algorithms
      PubDate: 2018-06-05
      DOI: 10.3390/a11060080
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 81: A Randomized Algorithm for Optimal PID

    • Authors: Yossi Peretz
      First page: 81
      Abstract: A randomized algorithm is suggested for the syntheses of optimal PID controllers for MIMO coupled systems, where the optimality is with respect to the H ∞ -norm, the H 2 -norm and the LQR functional, with possible system-performance specifications defined by regional pole-placement. Other notions of optimality (e.g., mixed H 2 / H ∞ design, controller norm or controller sparsity) can be handled similarly with the suggested algorithm. The suggested method is direct and thus can be applied to continuous-time systems as well as to discrete-time systems with the obvious minor changes. The presented algorithm is a randomized algorithm, which has a proof of convergence (in probability) to a global optimum.
      Citation: Algorithms
      PubDate: 2018-06-05
      DOI: 10.3390/a11060081
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 82: Research on Fault Diagnosis of a Marine
           Fuel System Based on the SaDE-ELM Algorithm

    • Authors: Yi Wei, Yaokun Yue
      First page: 82
      Abstract: Since the traditional fault diagnosis method of the marine fuel system has a low accuracy of identification, the algorithm solution can easily fall into local optimum, and they are not fit for the research on the fault diagnosis of a marine fuel system. Hence, a fault diagnosis method for a marine fuel system based on the SaDE-ELM algorithm is proposed. First, the parameters of initializing extreme learning machine are adopted by a differential evolution algorithm. Second, the fault diagnosis of the marine fuel system is realized by the fault diagnosis model corresponding to the state training of marine fuel system. Based on the obtained fault data of a marine fuel system, the proposed method is verified. The experimental results show that this method produces higher recognition accuracy and faster recognition speed that are superior to the traditional BP neural network, SVM support vector machine diagnosis algorithm, and the un-optimized extreme learning machine algorithm. The results have important significance relevant to fault diagnosis for a marine fuel system. The algorithm based on SaDE-ELM is an effective and practical method of fault diagnosis for a marine fuel system.
      Citation: Algorithms
      PubDate: 2018-06-07
      DOI: 10.3390/a11060082
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 83: A Combined Syntactical and Statistical
           Approach for R Peak Detection in Real-Time Long-Term Heart Rate
           Variability Analysis

    • Authors: David Pang, Tomohiko Igasaki
      First page: 83
      Abstract: Long-term heart rate variability (HRV) analysis is useful as a noninvasive technique for autonomic nervous system activity assessment. It provides a method for assessing many physiological and pathological factors that modulate the normal heartbeat. The performance of HRV analysis systems heavily depends on a reliable and accurate detection of the R peak of the QRS complex. Ectopic beats caused by misdetection or arrhythmic events can introduce bias into HRV results, resulting in significant problems in their interpretation. This study presents a novel method for long-term detection of normal R peaks (which represent the normal heartbeat in electrocardiographic signals), intended specifically for HRV analysis. The very low computational complexity of the proposed method, which combines and exploits the advantages of syntactical and statistical approaches, enables real-time applications. The approach was validated using the Massachusetts Institute of Technology–Beth Israel Hospital Normal Sinus Rhythm and the Fantasia database, and has a sensitivity, positive predictivity, detection error rate, and accuracy of 99.998, 99.999, 0.003, and 99.996%, respectively.
      Citation: Algorithms
      PubDate: 2018-06-07
      DOI: 10.3390/a11060083
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 84: Efficient Approximation for Restricted
           Biclique Cover Problems

    • Authors: Alessandro Epasto, Eli Upfal
      First page: 84
      Abstract: Covering the edges of a bipartite graph by a minimum set of bipartite complete graphs (bicliques) is a basic graph theoretic problem, with numerous applications. In particular, it is used to characterize parsimonious models of a set of observations (each biclique corresponds to a factor or feature that relates the observations in the two sets of nodes connected by the biclique). The decision version of the minimum biclique cover problem is NP-Complete, and unless P=NP, the cover size cannot be approximated in general within less than a sub-linear factor of the number of nodes (or edges) in the graph. In this work, we consider two natural restrictions to the problem, motivated by practical applications. In the first case, we restrict the number of bicliques a node can belong to. We show that when this number is at least 5, the problem is still NP-hard. In contrast, we show that when nodes belong to no more than two bicliques, the problem has efficient approximations. The second model we consider corresponds to observing a set of independent samples from an unknown model, governed by a possibly large number of factors. The model is defined by a bipartite graph G=(L,R,E), where each node in L is assigned to an arbitrary subset of up to a constant f factors, while the nodes in R (the independent observations) are assigned to random subsets of the set of k factors where k can grow with size of the graph. We show that this practical version of the biclique cover problem is amenable to efficient approximations.
      Citation: Algorithms
      PubDate: 2018-06-12
      DOI: 10.3390/a11060084
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 85: ILC with Initial State Learning for
           Fractional Order Linear Distributed Parameter Systems

    • Authors: Yong-Hong Lan, Zhe-Min Cui
      First page: 85
      Abstract: This paper presents a second order P-type iterative learning control (ILC) scheme with initial state learning for a class of fractional order linear distributed parameter systems. First, by analyzing the control and learning processes, a discrete system for P-type ILC is established, and the ILC design problem is then converted to a stability problem for such a discrete system. Next, a sufficient condition for the convergence of the control input and the tracking errors is obtained by introducing a new norm and using the generalized Gronwall inequality, which is less conservative than the existing one. Finally, the validity of the proposed method is verified by a numerical example.
      Citation: Algorithms
      PubDate: 2018-06-14
      DOI: 10.3390/a11060085
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 86: Performance Optimal PI controller Tuning
           Based on Integrating Plus Time Delay Models

    • Authors: Christer Dalen, David Di Ruscio
      First page: 86
      Abstract: A method for tuning PI controller parameters, a prescribed maximum time delay error or a relative time delay error is presented. The method is based on integrator plus time delay models. The integral time constant is linear in the relative time delay error, and the proportional constant is seen inversely proportional to the relative time delay error. The keystone in the method is the method product parameter, i.e., the product of the PI controller proportional constant, the integral time constant, and the integrator plus time delay model, velocity gain. The method product parameter is found to be constant for various PI controller tuning methods. Optimal suggestions are given for choosing the method product parameter, i.e., optimal such that the integrated absolute error or, more interestingly, the Pareto performance objective (i.e., integrated absolute error for combined step changes in output and input disturbances) is minimised. Variants of the presented tuning method are demonstrated for tuning PI controllers for motivated (possible) higher order process model examples, i.e., the presented method is combined with the model reduction step (process–reaction curve) in Ziegler–Nichols.
      Citation: Algorithms
      PubDate: 2018-06-17
      DOI: 10.3390/a11060086
      Issue No: Vol. 11, No. 6 (2018)
  • Algorithms, Vol. 11, Pages 56: BELMKN: Bayesian Extreme Learning Machines
           Kohonen Network

    • Authors: J. Senthilnath, Sumanth Simha C, Nagaraj G, Meenakumari Thapa, Indiramma M
      First page: 56
      Abstract: This paper proposes the Bayesian Extreme Learning Machine Kohonen Network (BELMKN) framework to solve the clustering problem. The BELMKN framework uses three levels in processing nonlinearly separable datasets to obtain efficient clustering in terms of accuracy. In the first level, the Extreme Learning Machine (ELM)-based feature learning approach captures the nonlinearity in the data distribution by mapping it onto a d-dimensional space. In the second level, ELM-based feature extracted data is used as an input for Bayesian Information Criterion (BIC) to predict the number of clusters termed as a cluster prediction. In the final level, feature-extracted data along with the cluster prediction is passed to the Kohonen Network to obtain improved clustering accuracy. The main advantage of the proposed method is to overcome the problem of having a priori identifiers or class labels for the data; it is difficult to obtain labels in most of the cases for the real world datasets. The BELMKN framework is applied to 3 synthetic datasets and 10 benchmark datasets from the UCI machine learning repository and compared with the state-of-the-art clustering methods. The experimental results show that the proposed BELMKN-based clustering outperforms other clustering algorithms for the majority of the datasets. Hence, the BELMKN framework can be used to improve the clustering accuracy of the nonlinearly separable datasets.
      Citation: Algorithms
      PubDate: 2018-04-27
      DOI: 10.3390/a11050056
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 57: Optimal Control Algorithms and Their
           Analysis for Short-Term Scheduling in Manufacturing Systems

    • Authors: Boris Sokolov, Alexandre Dolgui, Dmitry Ivanov
      First page: 57
      Abstract: Current literature presents optimal control computational algorithms with regard to state, control, and conjunctive variable spaces. This paper first analyses the advantages and limitations of different optimal control computational methods and algorithms which can be used for short-term scheduling. Second, it develops an optimal control computational algorithm that allows for the solution of short-term scheduling in an optimal manner. Moreover, qualitative and quantitative analysis of the manufacturing system scheduling problem is presented. Results highlight computer experiments with a scheduling software prototype as well as potential future research avenues.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050057
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 58: Computing Fault-Containment Times of
           Self-Stabilizing Algorithms Using Lumped Markov Chains

    • Authors: Volker Turau
      First page: 58
      Abstract: The analysis of self-stabilizing algorithms is often limited to the worst case stabilization time starting from an arbitrary state, i.e., a state resulting from a sequence of faults. Considering the fact that these algorithms are intended to provide fault tolerance in the long run, this is not the most relevant metric. A common situation is that a running system is an a legitimate state when hit by a single fault. This event has a much higher probability than multiple concurrent faults. Therefore, the worst case time to recover from a single fault is more relevant than the recovery time from a large number of faults. This paper presents techniques to derive upper bounds for the mean time to recover from a single fault for self-stabilizing algorithms based on Markov chains in combination with lumping. To illustrate the applicability of the techniques they are applied to a new self-stabilizing coloring algorithm.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050058
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 59: Decision-Making Approach Based on
           Neutrosophic Rough Information

    • Authors: Muhammad Akram, Nabeela Ishfaq, Sidra Sayed, Florentin Smarandache
      First page: 59
      Abstract: Rough set theory and neutrosophic set theory are mathematical models to deal with incomplete and vague information. These two theories can be combined into a framework for modeling and processing incomplete information in information systems. Thus, the neutrosophic rough set hybrid model gives more precision, flexibility and compatibility to the system as compared to the classic and fuzzy models. In this research study, we develop neutrosophic rough digraphs based on the neutrosophic rough hybrid model. Moreover, we discuss regular neutrosophic rough digraphs, and we solve decision-making problems by using our proposed hybrid model. Finally, we give a comparison analysis of two hybrid models, namely, neutrosophic rough digraphs and rough neutrosophic digraphs.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050059
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 60: Vessel Traffic Risk Assessment Based on
           Uncertainty Analysis in the Risk Matrix

    • Authors: Molin Sun, Zhongyi Zheng
      First page: 60
      Abstract: Uncertainty analysis is considered to be a necessary step in the process of vessel traffic risk assessment. The purpose of this study is to propose the uncertainty analysis algorithm which can be used to investigate the reliability of the risk assessment result. Probability and possibility distributions are used to quantify the two types of uncertainty identified in the risk assessment process. In addition, the algorithm for appropriate time window selection is chosen by considering the uncertainty of vessel traffic accident occurrence and the variation trend of the vessel traffic risk caused by maritime rules becoming operative. Vessel traffic accident data from the United Kingdom’s marine accident investigation branch are used for the case study. Based on a comparison with the common method of estimating the vessel traffic risk and the algorithm for uncertainty quantification without considering the time window selection, the availability of the proposed algorithms is verified, which can provide guidance for vessel traffic risk management.
      Citation: Algorithms
      PubDate: 2018-05-03
      DOI: 10.3390/a11050060
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 61: Relaxed Data Types as Consistency
           Conditions †

    • Authors: Edward Talmage, Jennifer L. Welch
      First page: 61
      Abstract: In the quest for higher-performance shared data structures, weakening consistency conditions and relaxing the sequential specifications of data types are two of the primary tools available in the literature today. In this paper, we show that these two approaches are in many cases different ways to specify the same sets of allowed concurrent behaviors of a given shared data object. This equivalence allows us to use whichever description is clearer, simpler, or easier to achieve equivalent guarantees. Specifically, for three common data type relaxations, we define consistency conditions such that the combination of the new consistency condition and an unrelaxed type allows the same behaviors as Linearizability and the relaxed version of the data type. Conversely, for the consistency condition k-Atomicity, we define a new data type relaxation such that the behaviors allowed by the relaxed version of a data type, combined with Linearizability, are the same as those allowed by k-Atomicity and the original type. As an example of the possibilities opened by our new equivalence, we use standard techniques from the literature on consistency conditions to prove that the three data type relaxations we consider are not comparable to one another or to several similar known conditions. Finally, we show a particular class of data types where one of our newly-defined consistency conditions is comparable to, and stronger than, one of the known consistency conditions we consider.
      Citation: Algorithms
      PubDate: 2018-05-04
      DOI: 10.3390/a11050061
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 62: A Feature-Weighted SVR Method Based on
           Kernel Space Feature

    • Authors: Minghua Xie, Decheng Wang, Lili Xie
      First page: 62
      Abstract: Support Vector Regression (SVR), which converts the original low-dimensional problem to a high-dimensional kernel space linear problem by introducing kernel functions, has been successfully applied in system modeling. Regarding the classical SVR algorithm, the value of the features has been taken into account, while its contribution to the model output is omitted. Therefore, the construction of the kernel space may not be reasonable. In the paper, a Feature-Weighted SVR (FW-SVR) is presented. The range of the feature is matched with its contribution by properly assigning the weight of the input features in data pre-processing. FW-SVR optimizes the distribution of the sample points in the kernel space to make the minimizing of the structural risk more reasonable. Four synthetic datasets and seven real datasets are applied. A superior generalization ability is obtained by the proposed method.
      Citation: Algorithms
      PubDate: 2018-05-04
      DOI: 10.3390/a11050062
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 63: The Supplier Selection of the Marine Rescue
           Equipment Based on the Analytic Hierarchy Process (AHP)-Limited Diversity
           Factors Method

    • Authors: Minghui Shao, Yan Song, Biao Wu, Yanjie Chang
      First page: 63
      Abstract: Supplier selection is an important decision-making link in bidding activity. When overall scores of several suppliers are similar, it is hard to obtain an accurate ranking of these suppliers. Applying the Diversity Factors Method (Diversity Factors Method, DFM) may lead to over correction of weights, which would degrade the capability of indexes to reflect the importance. A Limited Diversity Factors Method (Limited Diversity Factors Method, LDFM) based on entropy is presented in this paper in order to adjust the weights, in order to relieve the over correction in DFM and to improve the capability of identification of indexes in supplier selection. An example of salvage ship bidding demonstrates the advantages of the LDFM, in which the raking of overall scores of suppliers is more accurate.
      Citation: Algorithms
      PubDate: 2018-05-04
      DOI: 10.3390/a11050063
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 64: Utility Distribution Strategy of the Task
           Agents in Coalition Skill Games

    • Authors: Ming Lan Fu, Hao Wang, Bao Fu Fang
      First page: 64
      Abstract: This paper focuses on the rational distribution of task utilities in coalition skill games, which is a restricted form of coalition game, where each service agent has a set of skills and each task agent needs a set of skills in order to be completed. These two types of agents are assumed to be self-interested. Given the task selection strategy of service agents, the utility distribution strategies of task agents play an important role in improving their individual revenues and system total revenue. The problem that needs to be resolved is how to design the task selection strategies of the service agents and the utility distribution strategies of the task agents to make the self-interested decisions improve the system whole performance. However, to the best of our knowledge, this problem has been the topic of very few studies and has not been properly addressed. To address this problem, a task allocation algorithm for self-interested agents in a coalition skill game is proposed, it distributes the utilities of tasks to the needed skills according to the powers of the service agents that possess the corresponding skills. The final simulation results verify the effectiveness of the algorithm.
      Citation: Algorithms
      PubDate: 2018-05-05
      DOI: 10.3390/a11050064
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 65: Control Strategy of Speed Servo Systems
           Based on Deep Reinforcement Learning

    • Authors: Pengzhan Chen, Zhiqiang He, Chuanxi Chen, Jiahong Xu
      First page: 65
      Abstract: We developed a novel control strategy of speed servo systems based on deep reinforcement learning. The control parameters of speed servo systems are difficult to regulate for practical applications, and problems of moment disturbance and inertia mutation occur during the operation process. A class of reinforcement learning agents for speed servo systems is designed based on the deep deterministic policy gradient algorithm. The agents are trained by a significant number of system data. After learning completion, they can automatically adjust the control parameters of servo systems and compensate for current online. Consequently, a servo system can always maintain good control performance. Numerous experiments are conducted to verify the proposed control strategy. Results show that the proposed method can achieve proportional–integral–derivative automatic tuning and effectively overcome the effects of inertia mutation and torque disturbance.
      Citation: Algorithms
      PubDate: 2018-05-05
      DOI: 10.3390/a11050065
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 66: Single Machine Scheduling Problem with
           Interval Processing Times and Total Completion Time Objective

    • Authors: Yuri N. Sotskov, Natalja G. Egorova
      First page: 66
      Abstract: We consider a single machine scheduling problem with uncertain durations of the given jobs. The objective function is minimizing the sum of the job completion times. We apply the stability approach to the considered uncertain scheduling problem using a relative perimeter of the optimality box as a stability measure of the optimal job permutation. We investigated properties of the optimality box and developed algorithms for constructing job permutations that have the largest relative perimeters of the optimality box. Computational results for constructing such permutations showed that they provided the average error less than 0 . 74 % for the solved uncertain problems.
      Citation: Algorithms
      PubDate: 2018-05-07
      DOI: 10.3390/a11050066
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 67: Automated Processing of fNIRS Data—A
           Visual Guide to the Pitfalls and Consequences

    • Authors: Lia M. Hocke, Ibukunoluwa K. Oni, Chris C. Duszynski, Alex V. Corrigan, Blaise deB. Frederick, Jeff F. Dunn
      First page: 67
      Abstract: With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.
      Citation: Algorithms
      PubDate: 2018-05-08
      DOI: 10.3390/a11050067
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 68: Hybrid Flow Shop with Unrelated Machines,
           Setup Time, and Work in Progress Buffers for Bi-Objective Optimization of
           Tortilla Manufacturing

    • Authors: Victor Yaurima-Basaldua, Andrei Tchernykh, Francisco Villalobos-Rodríguez, Ricardo Salomon-Torres
      First page: 68
      Abstract: We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.
      Citation: Algorithms
      PubDate: 2018-05-09
      DOI: 10.3390/a11050068
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 69: A Multi-Stage Algorithm for a Capacitated
           Vehicle Routing Problem with Time Constraints

    • Authors: Lucia Cassettari, Melissa Demartini, Roberto Mosca, Roberto Revetria, Flavio Tonelli
      First page: 69
      Abstract: The Vehicle Routing Problem (VRP) is one of the most optimized tasks studied and it is implemented in a huge variety of industrial applications. The objective is to design a set of minimum cost paths for each vehicle in order to serve a given set of customers. Our attention is focused on a variant of VRP, the capacitated vehicle routing problem when applied to natural gas distribution networks. Managing natural gas distribution networks includes facing a variety of decisions ranging from human resources and material resources to facilities, infrastructures, and carriers. Despite the numerous papers available on vehicle routing problem, there are only a few that study and analyze the problems occurring in capillary distribution operations such as those found in a metropolitan area. Therefore, this work introduces a new algorithm based on the Saving Algorithm heuristic approach which aims to solve a Capacitated Vehicle Routing Problem with time and distance constraints. This joint algorithm minimizes the transportation costs and maximizes the workload according to customer demand within the constraints of a time window. Results from a real case study in a natural gas distribution network demonstrates the effectiveness of the approach.
      Citation: Algorithms
      PubDate: 2018-05-10
      DOI: 10.3390/a11050069
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 70: Estimating Functional Connectivity Symmetry
           between Oxy- and Deoxy-Haemoglobin: Implications for fNIRS Connectivity

    • Authors: Samuel Montero-Hernandez, Felipe Orihuela-Espina, Luis Sucar, Paola Pinti, Antonia Hamilton, Paul Burgess, Ilias Tachtsidis
      First page: 70
      Abstract: Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO2) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO2 and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO2 and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO2 and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO2 and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO2 and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO2 and HHb departs from random (t-test: t(509) = 26.39, p < 0.0001). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO2 and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable guideline for deciding whether to proceed with single or both Hb networks in FC analysis.
      Citation: Algorithms
      PubDate: 2018-05-11
      DOI: 10.3390/a11050070
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 71: Improving Monarch Butterfly Optimization
           Algorithm with Self-Adaptive Population

    • Authors: Hui Hu, Zhaoquan Cai, Song Hu, Yingxue Cai, Jia Chen, Sibo Huang
      First page: 71
      Abstract: Inspired by the migration behavior of monarch butterflies in nature, Wang et al. proposed a novel, promising, intelligent swarm-based algorithm, monarch butterfly optimization (MBO), for tackling global optimization problems. In the basic MBO algorithm, the butterflies in land 1 (subpopulation 1) and land 2 (subpopulation 2) are calculated according to the parameter p, which is unchanged during the entire optimization process. In our present work, a self-adaptive strategy is introduced to dynamically adjust the butterflies in land 1 and 2. Accordingly, the population size in subpopulation 1 and 2 are dynamically changed as the algorithm evolves in a linear way. After introducing the concept of a self-adaptive strategy, an improved MBO algorithm, called monarch butterfly optimization with self-adaptive population (SPMBO), is put forward. In SPMBO, only generated individuals who are better than before can be accepted as new individuals for the next generations in the migration operation. Finally, the proposed SPMBO algorithm is benchmarked by thirteen standard test functions with dimensions of 30 and 60. The experimental results indicate that the search ability of the proposed SPMBO approach significantly outperforms the basic MBO algorithm on most test functions. This also implies the self-adaptive strategy is an effective way to improve the performance of the basic MBO algorithm.
      Citation: Algorithms
      PubDate: 2018-05-14
      DOI: 10.3390/a11050071
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 72: Gray Wolf Optimization Algorithm for
           Multi-Constraints Second-Order Stochastic Dominance Portfolio Optimization

    • Authors: Yixuan Ren, Tao Ye, Mengxing Huang, Siling Feng
      First page: 72
      Abstract: In the field of investment, how to construct a suitable portfolio based on historical data is still an important issue. The second-order stochastic dominant constraint is a branch of the stochastic dominant constraint theory. However, only considering the second-order stochastic dominant constraints does not conform to the investment environment under realistic conditions. Therefore, we added a series of constraints into basic portfolio optimization model, which reflect the realistic investment environment, such as skewness and kurtosis. In addition, we consider two kinds of risk measures: conditional value at risk and value at risk. Most important of all, in this paper, we introduce Gray Wolf Optimization (GWO) algorithm into portfolio optimization model, which simulates the gray wolf’s social hierarchy and predatory behavior. In the numerical experiments, we compare the GWO algorithm with Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA). The experimental results show that GWO algorithm not only shows better optimization ability and optimization efficiency, but also the portfolio optimized by GWO algorithm has a better performance than FTSE100 index, which prove that GWO algorithm has a great potential in portfolio optimization.
      Citation: Algorithms
      PubDate: 2018-05-15
      DOI: 10.3390/a11050072
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 73: The NIRS Brain AnalyzIR Toolbox

    • Authors: Hendrik Santosa, Xuetong Zhai, Frank Fishburn, Theodore Huppert
      First page: 73
      Abstract: Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650–900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.
      Citation: Algorithms
      PubDate: 2018-05-16
      DOI: 10.3390/a11050073
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 74: Using Metaheuristics on the Multi-Depot
           Vehicle Routing Problem with Modified Optimization Criterion

    • Authors: Petr Stodola
      First page: 74
      Abstract: This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicles, whereas the criterion of modified MDVRP (M-MDVRP) is to minimize the longest route of all vehicles, i.e., the time to conduct the routing operation is as short as possible. For this problem, a metaheuristic algorithm—based on the Ant Colony Optimization (ACO) theory and developed by the author for solving the classic MDVRP instances—has been modified and adapted for M-MDVRP. In this article, an additional deterministic optimization process which further enhances the original ACO algorithm has been proposed. For evaluation of results, Cordeau’s benchmark instances are used.
      Citation: Algorithms
      PubDate: 2018-05-18
      DOI: 10.3390/a11050074
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 75: A New Oren–Nayar Shape-from-Shading
           Approach for 3D Reconstruction Using High-Order Godunov-Based Scheme

    • Authors: Guohui Wang, Yuanbo Chu
      First page: 75
      Abstract: 3D shape reconstruction from images has been an important topic in the field of robot vision. Shape-From-Shading (SFS) is a classical method for determining the shape of a 3D surface from a one intensity image. The Lambertian reflectance is a fundamental assumption in conventional SFS approaches. Unfortunately, when applied to characterize the reflection attribute of the diffuse reflection, the Lambertian model is tested to be inexact. In this paper, we present a new SFS approach for 3D reconstruction of diffuse surfaces whose reflection attribute is approximated by the Oren–Nayar reflection model. The partial differential Image Irradiance Equation (IIR) is set up with this model under a single distant point light source and an orthographic camera projection whose direction coincides with the light source. Then, the IIR is converted into an eikonal equation by solving a quadratic equation that includes the 3D surface shape. The viscosity solution of the resulting eikonal equation is approximated by using the high-order Godunov-based scheme that is accelerated by means of an alternating sweeping strategy. We conduct the experiments on synthetic and real-world images, and the experimental results illustrate the effectiveness of the presented approach.
      Citation: Algorithms
      PubDate: 2018-05-18
      DOI: 10.3390/a11050075
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 76: PHEFT: Pessimistic Image Processing
           Workflow Scheduling for DSP Clusters

    • Authors: Alexander Yu. Drozdov, Andrei Tchernykh, Sergey V. Novikov, Victor E. Vladislavlev, Raul Rivera-Rodriguez
      First page: 76
      Abstract: We address image processing workflow scheduling problems on a multicore digital signal processor cluster. We present an experimental study of scheduling strategies that include task labeling, prioritization, resource selection, and digital signal processor scheduling. We apply these strategies in the context of executing the Ligo and Montage applications. To provide effective guidance in choosing a good strategy, we present a joint analysis of three conflicting goals based on performance degradation. A case study is given, and experimental results demonstrate that a pessimistic scheduling approach provides the best optimization criteria trade-offs. The Pessimistic Heterogeneous Earliest Finish Time scheduling algorithm performs well in different scenarios with a variety of workloads and cluster configurations.
      Citation: Algorithms
      PubDate: 2018-05-22
      DOI: 10.3390/a11050076
      Issue No: Vol. 11, No. 5 (2018)
  • Algorithms, Vol. 11, Pages 77: A Novel Design of Sparse Prototype Filter
           for Nearly Perfect Reconstruction Cosine-Modulated Filter Banks

    • Authors: Wei Xu, Yi Li, Jinghong Miao, Jiaxiang Zhao, Xin Gao
      First page: 77
      Abstract: Cosine-modulated filter banks play a major role in digital signal processing. Sparse FIR filter banks have lower implementation complexity than full filter banks, while keeping a good performance level. This paper presents a fast design paradigm for sparse nearly perfect-reconstruction (NPR) cosine-modulated filter banks. First, an approximation function is introduced to reduce the non-convex quadratically constrained optimization problem to a linearly constrained optimization problem. Then, the desired sparse linear phase FIR prototype filter is derived through the orthogonal matching pursuit (OMP) performed under the weighted l 2 norm. The simulation results demonstrate that the proposed scheme is an effective paradigm to design sparse NPR cosine-modulated filter banks.
      Citation: Algorithms
      PubDate: 2018-05-22
      DOI: 10.3390/a11050077
      Issue No: Vol. 11, No. 5 (2018)
  • 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 49: Thermal Environment Prediction for Metro
           Stations Based on an RVFL Neural Network

    • Authors: Qing Tian, Weihang Zhao, Yun Wei, Liping Pang
      First page: 49
      Abstract: With the improvement of China’s metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.
      Citation: Algorithms
      PubDate: 2018-04-17
      DOI: 10.3390/a11040049
      Issue No: Vol. 11, No. 4 (2018)
  • Algorithms, Vol. 11, Pages 50: Evaluating Typical Algorithms of
           Combinatorial Optimization to Solve Continuous-Time Based Scheduling

    • Authors: Alexander A. Lazarev, Ivan Nekrasov, Nikolay Pravdivets
      First page: 50
      Abstract: We consider one approach to formalize the Resource-Constrained Project Scheduling Problem (RCPSP) in terms of combinatorial optimization theory. The transformation of the original problem into combinatorial setting is based on interpreting each operation as an atomic entity that has a defined duration and has to be resided on the continuous time axis meeting additional restrictions. The simplest case of continuous-time scheduling assumes one-to-one correspondence of resources and operations and corresponds to the linear programming problem setting. However, real scheduling problems include many-to-one relations which leads to the additional combinatorial component in the formulation due to operations competition. We research how to apply several typical algorithms to solve the resulted combinatorial optimization problem: enumeration including branch-and-bound method, gradient algorithm, random search technique.
      Citation: Algorithms
      PubDate: 2018-04-17
      DOI: 10.3390/a11040050
      Issue No: Vol. 11, No. 4 (2018)
  • Algorithms, Vol. 11, Pages 51: A Crowd Cooperative Spectrum Sensing
           Algorithm Using a Non-Ideal Channel

    • Authors: Xinxin Lv, Qi Zhu
      First page: 51
      Abstract: Spectrum sensing is the prerequisite of the realization of cognitive radio. So it is a significant part of cognitive radio. In order to stimulate the SUs to sense the spectrum, we combine the incentive mechanism of crowd-sensing with cooperative spectrum sensing effectively, and put forward a crowd cooperative spectrum sensing algorithm with optimal utility of secondary users (SUs) under non-ideal channel which we define SUs’ utility expectation functions related to rewards, sensing time and transmission power. Then, we construct the optimization problem of maximizing the utilities of SUs by optimizing the sensing time and the transmission power, and prove that this problem is a convex optimization problem. The optimal sensing time and transmission power are obtained by using the Karush-Kuhn-Tucker (KKT) conditions. The numerical simulation results show that the spectrum detection performance of algorithm, which we put forward, is improved.
      Citation: Algorithms
      PubDate: 2018-04-18
      DOI: 10.3390/a11040051
      Issue No: Vol. 11, No. 4 (2018)
  • Algorithms, Vol. 11, Pages 52: Introduction to Reconfiguration

    • Authors: Naomi Nishimura
      First page: 52
      Abstract: Reconfiguration is concerned with relationships among solutions to a problem instance, where the reconfiguration of one solution to another is a sequence of steps such that each step produces an intermediate feasible solution. The solution space can be represented as a reconfiguration graph, where two vertices representing solutions are adjacent if one can be formed from the other in a single step. Work in the area encompasses both structural questions (Is the reconfiguration graph connected') and algorithmic ones (How can one find the shortest sequence of steps between two solutions') This survey discusses techniques, results, and future directions in the area.
      Citation: Algorithms
      PubDate: 2018-04-19
      DOI: 10.3390/a11040052
      Issue No: Vol. 11, No. 4 (2018)
  • Algorithms, Vol. 11, Pages 53: Linking and Cutting Spanning Trees

    • Authors: Luís M. S. Russo, Andreia Sofia Teixeira, Alexandre P. Francisco
      First page: 53
      Abstract: We consider the problem of uniformly generating a spanning tree for an undirected connected graph. This process is useful for computing statistics, namely for phylogenetic trees. We describe a Markov chain for producing these trees. For cycle graphs, we prove that this approach significantly outperforms existing algorithms. For general graphs, experimental results show that the chain converges quickly. This yields an efficient algorithm due to the use of proper fast data structures. To obtain the mixing time of the chain we describe a coupling, which we analyze for cycle graphs and simulate for other graphs.
      Citation: Algorithms
      PubDate: 2018-04-19
      DOI: 10.3390/a11040053
      Issue No: Vol. 11, No. 4 (2018)
  • Algorithms, Vol. 11, Pages 54: Dual Market Facility Network Design under
           Bounded Rationality

    • Authors: D. G. Mogale, Geet Lahoti, Shashi Bhushan Jha, Manish Shukla, Narasimha Kamath, Manoj Kumar Tiwari
      First page: 54
      Abstract: A number of markets, geographically separated, with different demand characteristics for different products that share a common component, are analyzed. This common component can either be manufactured locally in each of the markets or transported between the markets to fulfill the demand. However, final assemblies are localized to the respective markets. The decision making challenge is whether to manufacture the common component centrally or locally. To formulate the underlying setting, a newsvendor modeling based approach is considered. The developed model is solved using Frank-Wolfe linearization technique along with Benders’ decomposition method. Further, the propensity of decision makers in each market to make suboptimal decisions leading to bounded rationality is considered. The results obtained for both the cases are compared.
      Citation: Algorithms
      PubDate: 2018-04-20
      DOI: 10.3390/a11040054
      Issue No: Vol. 11, No. 4 (2018)
  • Algorithms, Vol. 11, Pages 55: A Heuristic Approach to Solving the Train
           Traffic Re-Scheduling Problem in Real Time

    • Authors: Omid Gholami, Johanna Törnquist Krasemann
      First page: 55
      Abstract: Effectiveness in managing disturbances and disruptions in railway traffic networks, when they inevitably do occur, is a significant challenge, both from a practical and theoretical perspective. In this paper, we propose a heuristic approach for solving the real-time train traffic re-scheduling problem. This problem is here interpreted as a blocking job-shop scheduling problem, and a hybrid of the mixed graph and alternative graph is used for modelling the infrastructure and traffic dynamics on a mesoscopic level. A heuristic algorithm is developed and applied to resolve the conflicts by re-timing, re-ordering, and locally re-routing the trains. A part of the Southern Swedish railway network from Karlskrona centre to Malmö city is considered for an experimental performance assessment of the approach. The network consists of 290 block sections, and for a one-hour time horizon with around 80 active trains, the algorithm generates a solution in less than ten seconds. A benchmark with the corresponding mixed-integer program formulation, solved by commercial state-of-the-art solver Gurobi, is also conducted to assess the optimality of the generated solutions.
      Citation: Algorithms
      PubDate: 2018-04-21
      DOI: 10.3390/a11040055
      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

    • 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

    • 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

    • 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

    • 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)
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