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COMPUTER SCIENCE (1153 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 12)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 67)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 11)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 25)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 10)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal  
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 14)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 32)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 122)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 9)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 4)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 293)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 119)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 2)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 13)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access  
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 12)
Circuits and Systems     Open Access   (Followers: 16)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 11)
Communication Theory     Hybrid Journal   (Followers: 19)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 53)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 1)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 13)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 28)
Computer     Full-text available via subscription   (Followers: 83)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 7)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 14)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 7)
Computer Science and Engineering     Open Access   (Followers: 17)
Computer Science and Information Technology     Open Access   (Followers: 11)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)

        1 2 3 4 5 6 | Last

Journal Cover Applied Soft Computing
  [SJR: 1.763]   [H-I: 75]   [16 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1568-4946
   Published by Elsevier Homepage  [3042 journals]
  • Dynamic computing rough approximations approach to time-evolving
           information granule interval-valued ordered information system
    • Authors: Jianhang Yu; Minghao Chen; Weihua Xu
      Pages: 18 - 29
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Jianhang Yu, Minghao Chen, Weihua Xu
      With the advent of Big Data era has seen both the volumes and update rates of data increase rapidly. The granular structure of an information system is evolving with time when redundancy data leaves and new data arrives. In order to quickly achieve the rough approximations of dynamic attribute set interval-valued ordered information system that the attribute set varies over time. In this study, we proposed two dynamic computing rough approximations approaches for time-evolving information granule interval-valued ordered information system which induced by the deletion or addition some attributes, respectively. The updating mechanisms enable obtaining additional knowledge from the varied data without forgetting the prior knowledge. According to these established computing rules, two corresponding dynamic computing algorithms are designed and some examples are illustrated to explain updating principles and show computing process. Furthermore, a series of experiments were conducted to evaluate the computational efficiency of the studied updating mechanisms based on several UCI datasets. The experimental results clearly indicate that these methods significantly outperform the traditional approaches with a dramatic reduction in the computational efficiency to update the rough approximations.
      Graphical abstract image

      PubDate: 2017-07-03T01:38:23Z
      DOI: 10.1016/j.asoc.2017.06.009
      Issue No: Vol. 60 (2017)
  • Adaptive consensus model with multiplicative linguistic preferences based
           on fuzzy information granulation
    • Authors: Shitao Zhang; Jianjun Zhu; Xiaodi Liu; Ye Chen; Zhenzhen Ma
      Pages: 30 - 47
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Shitao Zhang, Jianjun Zhu, Xiaodi Liu, Ye Chen, Zhenzhen Ma
      An adaptive consensus model based on fuzzy information granulation (fuzzy IG) is presented for group consensus decision-making problems with multiplicative linguistic preference relations (MLPRs). Firstly, a granular representation of linguistic terms is concerned with the triangular fuzzy formation of a family of information granules over given Analytical Hierarchy Process (AHP) numerical scales. On this basis, the individual consistency and group consensus measure indices using fuzzy granulation technique are constructed, respectively. Then, the optimal cut-off points of fuzzy information granules are obtained by establishing a multi-objective optimization model together with a multi-objective particle swarm optimization (MOPSO) algorithm. A novel group consensus decision-making approach where consensus reaching process (CRP) is achieved by adaptively adjusting individual preferences through the optimization of the cut-off points is proposed. After conflict elimination, the obtained group preference gives the ranking of the alternatives. Finally, a real emergency decision-making case for liquid ammonia leak is given to illustrate the application steps of the proposed method and comparative analysis with the existing GDM methods. Comparative results demonstrate that the proposed method has some advantages in aspects of avoiding information loss or distortion and improving consensus performance.
      Graphical abstract image

      PubDate: 2017-07-03T01:38:23Z
      DOI: 10.1016/j.asoc.2017.06.028
      Issue No: Vol. 60 (2017)
  • Adaptive inverse position control of switched reluctance motor
    • Authors: Jia-Jun Wang
      Pages: 48 - 59
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Jia-Jun Wang
      In this paper, adaptive inverse position control is applied to switched reluctance motor (SRM) with simplified interval type-2 fuzzy neural networks (SIT2FNNs). The proposed adaptive inverse position control scheme for the SRM can be divided into the design of two control loops. The first loop is used for the position control, which is designed based on the adaptive inverse control (AIC). And the AIC is constructed with two SIT2FNNs, which are applied to identification and control for the SRM, respectively. The second loop is used for the current control, which is realized with the current-sharing method (CSM). Simulation results certify the effectiveness of the proposed control scheme in the achievement on high position control precision and perfect dynamic performance for the SRM.
      Graphical abstract image

      PubDate: 2017-07-03T01:38:23Z
      DOI: 10.1016/j.asoc.2017.06.014
      Issue No: Vol. 60 (2017)
  • Multimodal function optimizations with multiple maximums and multiple
           minimums using an improved PSO algorithm
    • Authors: Wei-Der Chang
      Pages: 60 - 72
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Wei-Der Chang
      In this paper, a multimodal function optimization problem consisting of multiple maximums and multiple minimums is solved using an improved particle swarm optimization (PSO) algorithm. In the proposed scheme, the original population needs to be randomly divided into two main groups in the first stage. One group is to tackle the maximum optimization of the multimodal function and the other then focuses on the function minimum optimization. In the second stage, each group is split up into several subgroups in order to seek for function optimums simultaneously. There is no relation among subgroups and each subgroup can individually seek for one of function optimums. To achieve that, it is necessary to enroll the best particle information of each subgroup. It means that the proposed structure contains a number of best particles, not a single global best particle. The third stage is to modify the velocity updating formula of the algorithm where the global best particle is simply replaced by the best particle of each subgroup. Under the proposed scheme, multiple maxima and minima of the multimodal function can probably be solved separately and synchronously. Finally, many different kinds of multimodal function problems are illustrated to certify the applicability of the presented method, including one maximum and one minimum, two maximums and two minimums, multiple maximums and multiple minimums, and a complex engineering optimization problem with inequality conditions.
      Graphical abstract image

      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.039
      Issue No: Vol. 60 (2017)
  • Solving the p-median bilevel problem with order through a hybrid heuristic
    • Authors: Martha-Selene Casas-Ramírez; José-Fernando Camacho-Vallejo
      Pages: 73 - 86
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Martha-Selene Casas-Ramírez, José-Fernando Camacho-Vallejo
      A variant of the p-median problem is considered and presented in this paper. This variant is based on the assumption that customers are free to choose the located facility that will serve them. The latter decision is made by considering the customers preferences towards the facilities. To study this problem, a mathematical bilevel programming formulation is proposed. Given the difficulty in solving such bilevel programs, two reformulations are used for solving the problem. The first reformulation adds constraints and variables to the mathematical model, while the second one adds only constraints. Yet, both reformulations avoid the need to solve an optimization problem parameterized by the upper level variables to find the value of the lower level variables. The results of numerical experiments show that the required time for both reformulations is significant increased as the size of the instance increases. Moreover, the reformulations are unable to solve the large-size instances. This led us to develop a hybrid heuristic algorithm based on scatter search, which obtains high-quality solutions for all tested instances in less time than is required by the abovementioned reformulations. Furthermore, the proposed heuristic was able to solve larger-size instances obtaining the optimal or currently best known solution. The registered results from the computational experimentation show that the proposed algorithm performs steadily. A comparison against a scatter search with random construction, a scatter search with greedy construction, a GRASP and a genetic algorithm shows that the proposed hybrid heuristic outperforms the other algorithms.
      Graphical abstract image

      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.026
      Issue No: Vol. 60 (2017)
  • On the robust PID adaptive controller for exoskeletons: A particle swarm
           optimization based approach
    • Authors: A. Belkadi; H. Oulhadj; Y. Touati; Safdar A. Khan; B. Daachi
      Pages: 87 - 100
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): A. Belkadi, H. Oulhadj, Y. Touati, Safdar A. Khan, B. Daachi
      This article proposes a robust PID adaptive controller for nonlinear systems with one or more degrees of freedom (DoF). The adaptive controller aims at minimizing the errors in trajectory tracking without requiring a prior modeling of the targeted nonlinear system. Furthermore, the proposed controller requires only the inputs and outputs of the system. And it is based on modified particle swarm optimization algorithm whose goal is to find the best PID parameters that optimize the execution of desired task by minimizing an objective function. The adaptation by the controller addresses two critical problems: The first problem is the instability of the control signal provided by the convergence phase of the classical PSO algorithm. This behavior adversely affects the lifetime of any actuator and, therefore, is undesirable. The second problem is the stagnation of the classical PSO algorithm after convergence at the immediately found optimal solution. The proposed adaptive PID controller is initially tested in simulation on a dynamical model of a robot manipulator evolving in the vertical plan. Which is followed by experimental tests performed on an actuated joint orthosis worn by human subjects having different morphologies. A comparative study with two other algorithms has been also conducted. Based on the obtained results, we conclude the efficiency of the proposed approach.
      Graphical abstract image

      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.012
      Issue No: Vol. 60 (2017)
  • Learning-based EM clustering for data on the unit hypersphere with
           application to exoplanet data
    • Authors: Miin-Shen Yang; Shou-Jen Chang-Chien; Wen-Liang Hung
      Pages: 101 - 114
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Miin-Shen Yang, Shou-Jen Chang-Chien, Wen-Liang Hung
      This study focuses on clustering algorithms for data on the unit hypersphere. This type of directional data lain on the surface of a unit hypersphere is used in geology, biology, meteorology, medicine and oceanography. The EM algorithm with mixtures of von Mises-Fisher distributions is often used for model-based clustering for data on the unit hypersphere. However, the EM algorithm is sensitive to initial values and outliers and a number of clusters must be assigned a priori. In this paper, we propose an effective approach, called a learning-based EM algorithm with von Mises-Fisher distributions, to cluster this type of hyper-spherical data. The proposed clustering method is robust to outliers, without the need for initialization, and automatically determines the number of clusters. Thus, it becomes a fully-unsupervised model-based clustering method for data on the unit hypersphere. Some numerical and real examples with comparisons are given to demonstrate the effectiveness and superiority of the proposed method. We also apply the proposed learning-based EM algorithm to cluster exoplanet data in extrasolar planets. The clustering results have several important implications for exoplanet data and allow an interpretation of exoplanet migration.
      Graphical abstract image

      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.037
      Issue No: Vol. 60 (2017)
  • Hierarchical genetic optimization of a fuzzy logic system for energy flows
           management in microgrids
    • Authors: Enrico De Santis; Antonello Rizzi; Alireza Sadeghian
      Pages: 135 - 149
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Enrico De Santis, Antonello Rizzi, Alireza Sadeghian
      Bio-inspired algorithms like Genetic Algorithms and Fuzzy Inference Systems (FIS) are nowadays widely adopted as hybrid techniques in improving goods and services. In this paper we present an interesting application of the fuzzy-GA paradigm to the problem of energy flows management in microgrids, concerning the design, through a data driven synthesis procedure, of an Energy Management System (EMS). The main aim consists in performing decision making for power flow management tasks in the proposed microgrid model, equipped by renewable sources and an energy storage system, aiming to maximize the accounting profit in energy trading with the main-grid. In particular this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set as the core inference engine of an an EMS. The HGA rationale focuses on a particular encoding scheme, based on control genes and parametric genes, applied to the optimization of the FIS parameters, allowing to perform a reduction in the structural complexity of the RB. A performance comparison is performed with a simpler approach based on a classic fuzzy-GA scheme, where both FIS parameters and rule weights are tuned, while the number of fuzzy rules is fixed in advance. Experiments shows how the fuzzy-HGA approach adopted for the synthesis of the proposed controller outperforms the classic fuzzy-GA scheme, increasing the accounting profit by 67% in the considered energy trading problem, yielding at the same time a simpler RB.
      Graphical abstract image Highlights

      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.05.059
      Issue No: Vol. 60 (2017)
  • Explicit formula of hedge-algebras-based fuzzy controller and applications
           in structural vibration control
    • Authors: Hai-Le Bui; Tung-Anh Le; Van-Binh Bui
      Pages: 150 - 166
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Hai-Le Bui, Tung-Anh Le, Van-Binh Bui
      In the present work, explicit formula or mathematical model of hedge-algebras-based controller (HAC) is proposed to represent relationship between control and state variables. In contrast to a conventional fuzzy controller (FC), when using Hedge Algebras (HA) theory to design HACs, their explicit formula or mathematical model can be expressed in term of a simple equation. Applications of HAC using the explicit formula in input time-delay and sliding-mode controls are also studied. Control performance of HACs is investigated by numerical simulations on active control of a building structure subjected to earthquake excitations, in which results obtained from the FC are also included for comparison.

      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.045
      Issue No: Vol. 60 (2017)
  • The new extension of TOPSIS method for multiple criteria decision making
           with hesitant Pythagorean fuzzy sets
    • Authors: Decui Liang; Zeshui Xu
      Pages: 167 - 179
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Decui Liang, Zeshui Xu
      Pythagorean fuzzy sets (PFSs) as a new generalization of fuzzy sets (FSs) can handle uncertain information more flexibly in the process of decision making. In our real life, we also may encounter a hesitant fuzzy environment. In view of the effective tool of hesitant fuzzy sets (HFSs) for expressing the hesitant situation, we introduce HFSs into PFSs and extend the existing research work of PFSs. Concretely speaking, this paper considers that the membership degree and the non-membership degree of PFSs are expressed as hesitant fuzzy elements. First, we propose a new concept of hesitant Pythagorean fuzzy sets (HPFSs) by combining PFSs with HFSs. It provides a new semantic interpretation for our evaluation. Meanwhile, the properties and the operators of HPFSs are studied in detail. For the sake of application, we focus on investigating the normalization method and the distance measures of HPFSs in advance. Then, we explore the application of HPFSs to multi-criteria decision making (MCDM) by employing the technique for order preference by similarity to ideal solution (TOPSIS) method. A new extension of TOPSIS method is further designed in the context of MCDM with HPFSs. Finally, an example of the energy project selection is presented to elaborate on the performance of our approach.

      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.034
      Issue No: Vol. 60 (2017)
  • Unit commitment by an improved binary quantum GSA
    • Authors: Fatemeh Barani; Mina Mirhosseini; Hossein Nezamabadi-pour; Malihe M. Farsangi
      Pages: 180 - 189
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Fatemeh Barani, Mina Mirhosseini, Hossein Nezamabadi-pour, Malihe M. Farsangi
      Unit commitment (UC) problem is an important optimizing task for scheduling the on/off states of generating units in power system operation over a time horizon such that the power generation cost is minimized. Since, increasing the number of generating units makes it difficult to solve in practice, many approaches have been introduced to solve the UC problem. This paper introduces an improved version of the binary quantum-inspired gravitational search algorithm (BQIGSA) and proposes a new approach to solve the UC problem based on the improved BQIGSA, called QGSA-UC. The proposed approach is applied to unit commitment problems with the number of generating units in the range of 10–120 along with 24-h scheduling horizon and is compared with nine state-of-the-art approaches. Furthermore, four different versions of gravitational approach are implemented for solving the UC problem and compared with those obtained by QGSA-UC. Comparative results clearly reveal the effectiveness of the proposed approach and show that it can be used as a reliable tool to solve UC problem.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.051
      Issue No: Vol. 60 (2017)
  • A credit ranking model for a parafinancial company based on the
           ELECTRE-III method and a multiobjective evolutionary algorithm
    • Authors: Diego Alonso Gastelum Chavira; Juan Carlos Leyva Lopez; Jesus Jaime Solano Noriega; Omar Ahumada Valenzuela; Pavel Anselmo Alvarez Carrillo
      Pages: 190 - 201
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Diego Alonso Gastelum Chavira, Juan Carlos Leyva Lopez, Jesus Jaime Solano Noriega, Omar Ahumada Valenzuela, Pavel Anselmo Alvarez Carrillo
      Credit rating is an assessment performed by lenders or financial institutions to determine a person’s creditworthiness based on the proposed terms of the loan. Frequently, these institutions use rating models to obtain estimates for the probabilities of default for their clients (companies, organizations, government, and individuals) and to assess the risk of credit portfolios. Numerous statistical and data mining methods are used to develop such models. In this paper, the potential of a multicriteria decision-aiding approach is studied. As a first step, the proposed methodology models the problem as a multicriteria evaluation process with multiple and in some cases, conflicting dimensions, which are integrated to derive sound recommendation for DMs. The second step of the methodology involves building a multicriteria outranking model based on ELECTRE III method. An evolutionary algorithm is used to exploit the outranking model. The methodology is applied to a small-scale financial institution operating in the agricultural sector. We compare loan applications based on their attributes and the credit profile of the customer or credit applicant. Our methodology offers the flexibility of combining heterogeneous information together with the preferences of decision makers (DMs), generating both relative and fixed rules for selecting the best loan applications among new and existing customers, which is an improvement over traditional methods The results reveal that outranking models are well suited to credit rating, providing good ranking results and suitable understanding on the relative importance of the evaluation criteria.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.021
      Issue No: Vol. 60 (2017)
  • The interactive consensus reaching process with the minimum and uncertain
           cost in group decision making
    • Authors: Yao Li; Hengjie Zhang; Yucheng Dong
      Pages: 202 - 212
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Yao Li, Hengjie Zhang, Yucheng Dong
      The consensus model with the minimum cost (or minimum adjustments or minimum information loss) is a powerful decision tool for consensus building in the group decision making (GDM). In the extant consensus models with the minimum cost, the unit adjustment cost of each expert is assumed to be exactly known, and an optimization-based consensus model is utilized to support the consensus building. In the practical GDM, however, it is difficult to obtain the exact unit adjustment costs, and the unit adjustment costs of experts are often uncertain. Moreover, we argue that the consensus cannot be achieved directly using the established optimization-based consensus model, because the consensus building is an interactive process that needs the participation of experts. This paper proposes an interactive consensus reaching process with the minimum and uncertain cost. In the consensus reaching process, an optimization-based consensus model with the uncertain unit cost is constructed to obtain the optimal adjusted opinions of experts. Then, the costs/resources are provided for experts to modify their opinions, and the obtained optimal adjusted opinions are used as a reference for the opinions-modifying in the consensus reaching process. Meanwhile, the unit adjustment costs of experts can be estimated according to the actual situation of the opinions-modifying in the consensus reaching process. The detailed numerical and simulation analysis are conducted to demonstrate the validity of the proposed consensus reaching model.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.056
      Issue No: Vol. 60 (2017)
  • Comparative Assessment of the Hybrid Genetic Algorithm–Artificial Neural
           Network and Genetic Programming Methods for the Prediction of Longitudinal
           Velocity Field around a Single Straight Groyne
    • Authors: Akbar Safarzadeh; Amir Hossein Zaji; Hossein Bonakdari
      Pages: 213 - 228
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Akbar Safarzadeh, Amir Hossein Zaji, Hossein Bonakdari
      In the present paper, three-dimensional flow fields around single straight groynes with various lengths have been discussed. The dataset of the flow field is measured in the laboratory using Acoustic Doppler Velocimeter (ADV). Then, the longitudinal velocity field is modelled using a novel hybrid method of Genetic Algorithm based artificial neural network (GAA) that has the ability to automatically adjust the number of hidden neurons. To investigate the proposed method’s performance, the results of GAA is measured and compared with one of the most common genetic algorithm based prediction method, namely genetic programming (GP). It is concluded that that GAA model successfully simulates the complex velocity field, and both the velocity magnitudes and isovel shapes are well predicted by this model. The results show that GAA with RMSE of 0.1236 in test data has a significantly better performance than the GP model with RMSE of 0.2342. In addition, it was founded that the transverse coordinate of the measuring point (Y*) is the most important input variable.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.048
      Issue No: Vol. 60 (2017)
  • Multivariate time series anomaly detection: A framework of Hidden Markov
    • Authors: Jinbo Li; Witold Pedrycz; Iqbal Jamal
      Pages: 229 - 240
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Jinbo Li, Witold Pedrycz, Iqbal Jamal
      In this study, we develop an approach to multivariate time series anomaly detection focused on the transformation of multivariate time series to univariate time series. Several transformation techniques involving Fuzzy C-Means (FCM) clustering and fuzzy integral are studied. In the sequel, a Hidden Markov Model (HMM), one of the commonly encountered statistical methods, is engaged here to detect anomalies in multivariate time series. We construct HMM-based anomaly detectors and in this context compare several transformation methods. A suite of experimental studies along with some comparative analysis is reported.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.035
      Issue No: Vol. 60 (2017)
  • Comparative study of pheromone control heuristics in ACO algorithms for
           solving RCPSP problems
    • Authors: Antonio Gonzalez-Pardo; Javier Del Ser; David Camacho
      Pages: 241 - 255
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Antonio Gonzalez-Pardo, Javier Del Ser, David Camacho
      Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.042
      Issue No: Vol. 60 (2017)
  • A modified ant system to achieve better balance between intensification
           and diversification for the traveling salesman problem
    • Authors: Yuzhe Yan; Han-suk Sohn; German Reyes
      Pages: 256 - 267
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Yuzhe Yan, Han-suk Sohn, German Reyes
      This paper presents a new variant of Ant Colony Optimization (ACO) for the Traveling Salesman Problem (TSP). ACO has been successfully used in many combinatorial optimization problems. However, ACO has a problem in reaching the global optimal solutions for TSPs, and the algorithmic performance of ACO tends to deteriorate significantly as the problem size increases. In the proposed modification, adaptive tour construction and pheromone updating strategies are embedded into the conventional Ant System (AS), to achieve better balance between intensification and diversification in the search process. The performance of the proposed algorithm is tested on randomly generated data and well-known existing data. The computational results indicate the proposed modification is effective and efficient for the TSP and competitive with Ant Colony System (ACS), Max-Min Ant System (MMAS), and Artificial Bee Colony (ABC) Meta-Heuristic.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.049
      Issue No: Vol. 60 (2017)
  • A multiobjective approach for optimal placement and sizing of distributed
           generators and capacitors in distribution network
    • Authors: Partha P. Biswas; R. Mallipeddi; P.N. Suganthan; Gehan A.J. Amaratunga
      Pages: 268 - 280
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Partha P. Biswas, R. Mallipeddi, P.N. Suganthan, Gehan A.J. Amaratunga
      Both active and reactive power play important roles in power system transmission and distribution networks. While active power does the useful work, reactive power supports the voltage that necessitates control from system reliability aspect as deviation of voltage from nominal range may lead to inadvertent operation and premature failure of system components. Reactive power flow must also be controlled in the system to maximize the amount of real power that can be transferred across the power transmitting media. This paper proposes an approach to simultaneously minimize the real power loss and the net reactive power flow in the system when reinforced with distributed generators (DGs) and shunt capacitors (SCs). With the suggested method, the system performance, reliability and loading capacity can be increased by reduction of losses. A multiobjective evolutionary algorithm based on decomposition (MOEA/D) is adopted to select optimal sizes and locations of DGs and SCs in large scale distribution networks with objectives being minimizing system real and reactive power losses. MOEA/D is the process of decomposition of a multiobjective optimization problem into a number of scalar optimization subproblems and optimizing those concurrently. Case studies with standard IEEE 33-bus, 69-bus, 119-bus distribution networks and a practical 83-bus distribution network are performed. Output results of MOEA/D method are compared with similar past studies and notable improvement is observed.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.07.004
      Issue No: Vol. 60 (2017)
  • Generation of Particle Swarm Optimization algorithms: An experimental
           study using Grammar-Guided Genetic Programming
    • Authors: Péricles B.C. Miranda; Ricardo B.C. Prudêncio
      Pages: 281 - 296
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Péricles B.C. Miranda, Ricardo B.C. Prudêncio
      Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. Grammar-Guided Genetic Programming (GGGP) algorithms, in particular, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar, differently from methods that simply select designs in a pre-defined limited search space. Although GGGP algorithms have been largely used in other contexts, they have not been deeply investigated in the generation of PSO algorithms. Thus, this work applies GGGP algorithms in the context of PSO algorithm design problem. Herein, we performed an experimental study comparing different GGGP approaches for the generation of PSO algorithms. The main goal is to perform a deep investigation aiming to identify pros and cons of each approach in the current task. In the experiments, a comparison between a tree-based GGGP approach and commonly used linear GGGP approaches for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-the-art optimization algorithms, and it achieved competitive results.
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      PubDate: 2017-07-12T03:58:40Z
      DOI: 10.1016/j.asoc.2017.06.040
      Issue No: Vol. 60 (2017)
  • Expected hesitant VaR for tail decision making under probabilistic
           hesitant fuzzy environment
    • Authors: Wei Zhou; Zeshui Xu
      Pages: 297 - 311
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Wei Zhou, Zeshui Xu
      Generally, to reasonably make decision, all evaluation information should be aggregated, and thus, the ranking and the optimal alternative can be obtained. However, in some extreme cases, the decision maker (DM) can only focus on the tail information such as the big-loss or big-gain values and wants to ask the simple question “How bad can a thing become?” or “How good can a thing become?” To address this type of decision-making issue, this paper introduces the definition of value at risk (VaR), which is a famous term in the financial field, and the probabilistic hesitant fuzzy element (PHFE), which is a general hesitant fuzzy element (HFE) and has recently become a popular topic. Then, the hesitant VaR (HVaR) is defined, and its mathematical presentation is provided to measure the tail information of the PHFEs. It is found that the tail information calculated by the HVaR is segmentary, and only the boundary value is used. Therefore, this paper further develops the expected HVaR (EHVaR) to improve the HVaR, which can describe the entire tail information. Two simple examples are provided to show and compare the proposed HVaR and EHVaR. To apply the EHVaR into a group decision making that focuses on the tail information, this paper proposes a dynamic programming model to calculate the weights of the DMs based on the principle that the more accurate PHFE should be given a bigger weight. Then, the tail group decision making steps based on the EHVaR are presented. Finally, this paper provides an example of selecting the optimal stock for four newly listed stocks in China to demonstrate the effectiveness of the proposed approaches.
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      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.06.057
      Issue No: Vol. 60 (2017)
  • A two-stage fuzzy multi-objective framework for segmentation of 3D MRI
           brain image data
    • Authors: Sayan Kahali; Sudip Kumar Adhikari; Jamuna Kanta Sing
      Pages: 312 - 327
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Sayan Kahali, Sudip Kumar Adhikari, Jamuna Kanta Sing
      Segmentation of Magnetic Resonance Imaging (MRI) brain image data has a significant impact on the computer guided medical image diagnosis and analysis. However, due to limitation of image acquisition devices and other related factors, MRI images are severely affected by the noise and inhomogeneity artefacts which lead to blurry edges in the intersection of the intra-organ soft tissue regions, making the segmentation process more difficult and challenging. This paper presents a novel two-stage fuzzy multi-objective framework (2sFMoF) for segmenting 3D MRI brain image data. In the first stage, a 3D spatial fuzzy c-means (3DSpFCM) algorithm is introduced by incorporating the 3D spatial neighbourhood information of the volume data to define a new local membership function along with the global membership function for each voxel. In particular, the membership functions actually define the underlying relationship between the voxels of a close cubic neighbourhood and image data in 3D image space. The cluster prototypes thus obtained are fed into a 3D modified fuzzy c-means (3DMFCM) algorithm, which further incorporates local voxel information to generate the final prototypes. The proposed framework addresses the shortcomings of the traditional FCM algorithm, which is highly sensitive to noise and may stuck into a local minima. The method is validated on a synthetic image volume and several simulated and in-vivo 3D MRI brain image volumes and found to be effective even in noisy data. The empirical results show the supremacy of the proposed method over the other FCM based algorithms and other related methods devised in the recent past.
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      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.07.001
      Issue No: Vol. 60 (2017)
  • Quaternion-based Deep Belief Networks fine-tuning
    • Authors: João Paulo Papa; Gustavo H. Rosa; Danillo R. Pereira; Xin-She Yang
      Pages: 328 - 335
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): João Paulo Papa, Gustavo H. Rosa, Danillo R. Pereira, Xin-She Yang
      Deep learning techniques have been paramount in the last years, mainly due to their outstanding results in a number of applications. In this paper, we address the issue of fine-tuning parameters of Deep Belief Networks by means of meta-heuristics in which real-valued decision variables are described by quaternions. Such approaches essentially perform optimization in fitness landscapes that are mapped to a different representation based on hypercomplex numbers that may generate smoother surfaces. We therefore can map the optimization process onto a new space representation that is more suitable to learning parameters. Also, we proposed two approaches based on Harmony Search and quaternions that outperform the state-of-the-art results obtained so far in three public datasets for the reconstruction of binary images.
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      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.06.046
      Issue No: Vol. 60 (2017)
  • Improved solution to the non-domination level update problem
    • Authors: Sumit Mishra; Samrat Mondal; Sriparna Saha
      Pages: 336 - 362
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Sumit Mishra, Samrat Mondal, Sriparna Saha
      In this paper, we present two approaches for non-domination level update problem. The first one is a space efficient non-domination level update (SENLU) approach. The second one is a binary search tree based efficient non-domination level update (BST-ENLU) approach which uses the basic property of binary search tree. Although the space complexity of BST-ENLU approach is higher than SENLU approach in case of insertion, but in terms of number of dominance comparisons, BST-ENLU approach can outperform SENLU approach. Thus, these two approaches are complementary to each other. The comparative results show that in case where all the solutions are in different fronts, the maximum number of dominance comparisons using BST-ENLU approach is very less than ENLU approach. A tree based approach is introduced to identify the correct position of the solution to be deleted efficiently. Also a theoretical upper bound to the maximum number of dominance comparisons is obtained for both the proposed approaches in case of both insertion and deletion operations.

      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.06.038
      Issue No: Vol. 60 (2017)
  • Effective local search algorithms for high school timetabling problems
    • Authors: Landir Saviniec; Ademir Aparecido Constantino
      Pages: 363 - 373
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Landir Saviniec, Ademir Aparecido Constantino
      This paper addresses the high school timetabling problem. The problem consists in building weekly timetables for meetings between classes and teachers with the goal of minimizing violations of specific requirements. In the last decades, several mixed-integer programs have been proposed and tested for this family of problems. However, medium and large size instances are still not effectively solved by these programs using state-of-the-art solvers and the scientific community has given special attention to the devising of alternative soft computing algorithms. In this paper, we propose a soft computing approach based on Iterated Local Search and Variable Neighborhood Search metaheuristic frameworks. Our algorithms incorporate new neighborhood structures and local search routines to perform an effective search. We validated the proposed algorithms on variants of the problem using seven public instances and a new dataset with 34 real-world instances including large cases. The results demonstrate that the proposed algorithms outperform the state-of-the-art approaches in both cases, finding the best solutions in 38 out of the 41 tested instances.

      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.06.047
      Issue No: Vol. 60 (2017)
  • A multiobjective robust controller synthesis approach aided by
           multicriteria decision analysis
    • Authors: Wagner Eustáquio Gomes Bachur; Eduardo Nunes Gonçalves; Jaime Arturo Ramírez; Lucas S. Batista
      Pages: 374 - 386
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Wagner Eustáquio Gomes Bachur, Eduardo Nunes Gonçalves, Jaime Arturo Ramírez, Lucas S. Batista
      This paper proposes a methodology for robust dynamic output-feedback control synthesis of uncertain linear systems represented by polytopic models. This control problem results in a semi-infinite optimization problem. The proposed synthesis procedure improves a previous two-step procedure, composed of synthesis and analysis, by employing both a multiobjective evolutionary algorithm (MOEA) and a multiple criteria decision making (MCDM) strategy in the synthesis step. The analysis stage is performed via a Branch and Bound (B&B) algorithm, enabling the validation of the former step. In the proposed multiobjective approach, the project aim is to meet the specifications of (i) reference signal tracking response, (ii) disturbance rejection and (iii) measurement noise attenuation. The project specifications are quantified in terms of H ∞ and H 2 norms of closed-loop transfer functions. Essentially, this technique combines the flexibility of a dedicated MOEA together with the minimax semi-infinite programming problem. Since a MOEA evolves a set of candidate solutions in parallel, the suggested strategy provides a diverse set of controller designs, which is very useful for an a posteriori decision-making process. A proposed multicriteria decision-aid strategy is employed, in addition, as a controller design tool, aiming to incorporate the decision-maker preferences throughout the process, which (i) enables a guided evolutionary search to a trade-off region (of solutions) of practical interest and (ii) assists the definition of an adequate final controller, characterized by a reasonable practical trade-off concerning the criteria. The application of the suggested framework is illustrated on three case studies in order to demonstrate the effectiveness of the method presented.
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      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.06.027
      Issue No: Vol. 60 (2017)
  • Slope stability evaluation using Gaussian processes with various
           covariance functions
    • Authors: Fei Kang; Bin Xu; Junjie Li; Sizeng Zhao
      Pages: 387 - 396
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Fei Kang, Bin Xu, Junjie Li, Sizeng Zhao
      This paper presents a stability evaluation method for slopes based on Gaussian processes (GPs), which is a popular machine learning technique for nonlinear system modeling. Covariance function is one of the most critical parts in GPs modeling, because it determines the properties of sample functions drawn from the Gaussian process prior. Sixteen covariance functions are tested on several datasets for slope stability evaluation problems. Experimental: results show that GPs models can reflect the complex relationship between input and output variables. The obtained results are better or similar to the results obtained by several other existing methods, such as artificial neural networks, support vector machines, etc. The other important attractions of GPs include a simple training process and a predictive distribution of the system output.
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      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.07.011
      Issue No: Vol. 60 (2017)
  • Implementation of regularization for separable nonlinear least squares
    • Authors: Xiaoyong Zeng; Hui Peng; Feng Zhou; Yanhui Xi
      Pages: 397 - 406
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Xiaoyong Zeng, Hui Peng, Feng Zhou, Yanhui Xi
      This paper considers a class of separable nonlinear least squares problems in which a model can be represented as a linear combination of nonlinear functions. A regularized nonlinear parameter optimization approach is presented for coping with the potential ill-conditioned problem of parameter divergence. Together with a regularization parameter detection technique, Tikhonov regularization and truncated singular value decomposition are utilized in the estimation of the linear parameters if the nonlinear parameters are changed during the parameter optimization process, which centers on a nonlinear parameter search using the Levenberg-Marquardt algorithm. Benefiting from the regularization in parameter optimization, the potential ill-conditioned issue can be avoided, and the multi-step-ahead forecasting accuracy of the estimated model may be largely improved. The usefulness of this approach is illustrated by means of a chaotic time-series prediction and nonlinear industrial process modeling.
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      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.07.006
      Issue No: Vol. 60 (2017)
  • A novel hybridization strategy for krill herd algorithm applied to
           clustering techniques
    • Authors: Laith Mohammad Abualigah; Ahamad Tajudin Khader; Essam Said Hanandeh; Amir Hossein Gandomi
      Pages: 423 - 435
      Abstract: Publication date: November 2017
      Source:Applied Soft Computing, Volume 60
      Author(s): Laith Mohammad Abualigah, Ahamad Tajudin Khader, Essam Said Hanandeh, Amir Hossein Gandomi
      Krill herd (KH) is a stochastic nature-inspired optimization algorithm that has been successfully used to solve numerous complex optimization problems. This paper proposed a novel hybrid of KH algorithm with harmony search (HS) algorithm, namely, H-KHA, to improve the global (diversification) search ability. The enhancement includes adding global search operator (improvise a new solution) of the HS algorithm to the KH algorithm for improving the exploration search ability by a new probability factor, namely, Distance factor, thereby moving krill individuals toward the best global solution. The effectiveness of the proposed H-KHA is tested on seven standard datasets from the UCI Machine Learning Repository that are commonly used in the domain of data clustering, also six common text datasets that are used in the domain of text document clustering. The experiments reveal that the proposed hybrid KHA with HS algorithm (H-KHA) enhanced the results in terms of accurate clusters and high convergence rate. Mostly, the performance of H-KHA is superior or at least highly competitive with the original KH algorithm, well-known clustering techniques and other comparative optimization algorithms.

      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.06.059
      Issue No: Vol. 60 (2017)
  • Novel fuzzy linguistic based mathematical model to assess risk of invasive
           alien plant species
    • Authors: H.O.W. Peiris; S. Chakraverty; S.S.N. Perera; S.M.W. Ranwala
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): H.O.W. Peiris, S. Chakraverty, S.S.N. Perera, S.M.W. Ranwala
      The purpose of this study is to develop a mathematical model for risk assessment that evaluates the invasion risk of invasive alien plant species based on their biological attributes. Data for most of the attributes were qualitative and in the form of linguistic terms. In order to handle the numerical and linguistic variables, we proposed three models in the fuzzy environment. In the first model, all the selected attributes were considered as equally important to invasiveness and in the second and third models, these are considered to be unequally important. The important weights for the biological attributes in these models were gathered from the group of experts in plant sciences. Proposed Model III incorporates more sophisticated linguistic tool than Model II. Model II gives better predictions in comparison to the first and third models and it is found to be better tracking system for identifying potential invaders as in the case of conventional risk assessment method.
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      PubDate: 2017-06-21T14:33:54Z
      DOI: 10.1016/j.asoc.2017.06.006
      Issue No: Vol. 59 (2017)
  • A method of defuzzification based on the approach of areas' ratio
    • Authors: Maxim V. Bobyr; Natalya A. Milostnaya; Sergey A. Kulabuhov
      Pages: 19 - 32
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Maxim V. Bobyr, Natalya A. Milostnaya, Sergey A. Kulabuhov
      The new method of defuzzification of output parameters from the base of fuzzy rules for a Mamdani fuzzy controller is given in the paper. The peculiarity of the method is the usage of the universal equation for the area computation of the geometric shapes. During the realization of fuzzy inference linguistic terms, the structure changes from the triangular into a trapezoidal shape. That is why the universal equation is used. The method is limited and can be used only for the triangular and trapezoidal membership functions. Gaussian functions can also be used while modifying the proposed method. Traditional defuzzification models such as Middle of Maxima − MoM, First of Maxima − FoM, Last of Maxima − LoM, First of Suppport − FoS, Last of Support − LoS, Middle of Support − MoS, Center of Sums − CoS, Model of Height − MoH have a number of systematic errors: curse of dimensionality, partition of unity condition and absence of additivity. The above-mentioned methods can be seen as Center of Gravity − CoG, which has the same errors. These errors lead to the fact that accuracy of fuzzy systems decreases, because during the training root mean square error increases. One of the reasons that provokes the errors is that some of the activated fuzzy rules are excluded from the fuzzy inference. It is also possible to increase the accuracy of the fuzzy system through properties of continuity. The proposed method guarantees fulfilling of the property of continuity, as the intersection point of the adjustment linguistic terms equals 0.5 when a parametrized membership function is used. The causes of errors and a way to delete them are reviewed in the paper. The proposed method excludes errors which are inherent to the traditional and non- traditional models of defuzzification. Comparative analysis of the proposed method of defuzzification with traditional and non-traditional models shows its effectiveness.
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      PubDate: 2017-06-02T02:21:18Z
      DOI: 10.1016/j.asoc.2017.05.040
      Issue No: Vol. 59 (2017)
  • Multi-objective differential evolution with performance-metric-based
           self-adaptive mutation operator for chemical and biochemical dynamic
           optimization problems
    • Authors: Qinqin Fan; Weili Wang; Xuefeng Yan
      Pages: 33 - 44
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Qinqin Fan, Weili Wang, Xuefeng Yan
      Each mutation operator of differential evolution (DE) algorithm is generally suitable for certain specific types of multi-objective optimization problems (MOPs) or particular stages of the evolution. To automatically select an appropriate mutation operator for solving MOPs in different phases of the evolution, a multi-objective differential evolution with performance-metric-based self-adaptive mutation operator (MODE-PMSMO) is proposed in this study. In MODE-PMSMO, a modified inverted generational distance (IGD) is utilized to evaluate the performance of each mutation operator and guide the evolution of mutation operators. The proposed MODE-PMSMO is then compared with seven multi-objective evolutionary algorithms (MOEAs) on five bi-objective and five tri-objective optimization problems. Generally, MODE-PMSMO exhibits the best average performance among all compared algorithms on ten MOPs. Additionally, MODE-PMSMO is employed to solve four typical multi-objective dynamic optimization problems in chemical and biochemical processes. Experimental results indicate that MODE-PMSMO is suitable for solving these actual problems and can provide a set of nondominated solutions for references of decision makers.
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      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.05.044
      Issue No: Vol. 59 (2017)
  • Distributed generation allocation with on-load tap changer on radial
           distribution networks using adaptive genetic algorithm
    • Authors: Sanjib Ganguly; Dipanjan Samajpati
      Pages: 45 - 67
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Sanjib Ganguly, Dipanjan Samajpati
      This paper presents a distribution generation (DG) allocation strategy along with on-load tap changer (OLTC) for radial distribution networks using adaptive genetic algorithm (AGA). The optimal locations for DG units and OLTC, the optimal generation for the DG units, and the OLTC tap ratio are determined by minimizing a weighted objective function comprising of network power loss and maximum bus voltage deviation. A planning model incorporating DG and OLTC models is devised. Three different types of DG units, i.e., DG operating at lagging, leading, and unity power factor are used in the planning. Two new AGA variants based on adaptively varying crossover and mutation probabilities are proposed and used in this work as solution strategies and their performances are compared with some of the existing similar type of AGA variants using the results of multiple runs. The performances of the proposed AGA variants are found to be better in terms of quality of the solutions and consistency. The results also show that the combined operation of DG and OLTC reduces significant amount of power loss and bus voltage deviation. A direct performance comparison with some of the similar approaches shows that the solutions obtained with the proposed approach provide similar amount of power loss reduction and bus voltage improvement with lesser amount of DG penetration level. The approach is demonstrated on a 69-bus test distribution network and a 52-bus Indian practical distribution network.
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      PubDate: 2017-06-02T02:21:18Z
      DOI: 10.1016/j.asoc.2017.05.041
      Issue No: Vol. 59 (2017)
  • A hierarchical global path planning approach for mobile robots based on
           multi-objective particle swarm optimization
    • Authors: Thi Thoa Mac; Cosmin Copot; Duc Trung Tran; Robin De Keyser
      Pages: 68 - 76
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Thi Thoa Mac, Cosmin Copot, Duc Trung Tran, Robin De Keyser
      In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach.
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      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.05.012
      Issue No: Vol. 59 (2017)
  • A study of overfitting in optimization of a manufacturing quality control
    • Authors: Tea Tušar; Klemen Gantar; Valentin Koblar; Bernard Ženko; Bogdan Filipič
      Pages: 77 - 87
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Tea Tušar, Klemen Gantar, Valentin Koblar, Bernard Ženko, Bogdan Filipič
      Quality control of the commutator manufacturing process can be automated by means of a machine learning model that can predict the quality of commutators as they are being manufactured. Such a model can be constructed by combining machine vision, machine learning and evolutionary optimization techniques. In this procedure, optimization is used to minimize the model error, which is estimated using single cross-validation. This work exposes the overfitting that emerges in such optimization. Overfitting is shown for three machine learning methods with different sensitivity to it (trees, additionally pruned trees and random forests) and assessed in two ways (repeated cross-validation and validation on a set of unseen instances). Results on two distinct quality control problems show that optimization amplifies overfitting, i.e., the single cross-validation error estimate for the optimized models is overly optimistic. Nevertheless, minimization of the error estimate by single cross-validation in general results in minimization of the other error estimates as well, showing that optimization is indeed beneficial in this context.
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      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.05.027
      Issue No: Vol. 59 (2017)
  • An improved MOEA/D algorithm for multi-objective multicast routing with
           network coding
    • Authors: Huanlai Xing; Zhaoyuan Wang; Tianrui Li; Hui Li; Rong Qu
      Pages: 88 - 103
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Huanlai Xing, Zhaoyuan Wang, Tianrui Li, Hui Li, Rong Qu
      Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time.
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      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.05.033
      Issue No: Vol. 59 (2017)
  • Prey predator hyperheuristic
    • Authors: Surafel Luleseged Tilahun
      Pages: 104 - 114
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Surafel Luleseged Tilahun
      Prey predator algorithm is a population based metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, a solution with a better performance is called best prey and focuses totally on exploitation whereas the solution with least performance is called predator and focuses totally on exploration. The remaining solutions are called ordinary prey and either exploit promising regions by following better performing solutions or explore the solution space by randomly running away from the predator. Recently, it has been shown that by increasing the number of best prey or predator, it is possible to adjust the degree of exploitation and exploration. Even though, this tuning has the advantage of easily controlling these search behaviors, it is not an easy task. As any other metaheuristic algorithm, the performance of prey predator algorithm depends on the proper degree of exploration and exploitation of the decision space. In this paper, the concept of hyperheuristic is employed to balance the degree of exploration and exploitation of the algorithm. So that it learns and decides the best search behavior for the problem at hand in iterations. The ratio of the number of the best prey and the predators are used as low level heuristics. From the simulation results the balancing of the degree of exploration and exploitation by using hyperheuristic mechanism indeed improves the performance of the algorithm. Comparison with other algorithms shows the effectiveness of the proposed approach.
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      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.04.044
      Issue No: Vol. 59 (2017)
  • Safety management of waterway congestions under dynamic risk
           conditions—A case study of the Yangtze River
    • Authors: X.P. Yan; C.P. Wan; D. Zhang; Z.L. Yang
      Pages: 115 - 128
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): X.P. Yan, C.P. Wan, D. Zhang, Z.L. Yang
      With the continuous increase of traffic volume in recent years, inland waterway transportation suffers more and more from congestion problems, which form a major impediment to its development. Thus, it is of great significance for the stakeholders and decision makers to address these congestion issues properly. Fuzzy Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) is widely used for solving Multiple Criteria Decision Making (MCDM) problems with ambiguity. When taking into account fuzzy TOPSIS, decisions are made in a static scenario with fixed weights assigned to the criteria. However, risk conditions usually vary in real-life cases, which will inevitably affect the preference ranking of the alternatives. To make flexible decisions according to the dynamics of congestion risks and to achieve a rational risk analysis for prioritising congestion risk control options (RCOs), the cost-benefit ratio (CBR) is used in this paper to reflect the change of risk conditions. The hybrid of CBR and fuzzy TOPSIS is illustrated by investigating the congestion risks of the Yangtze River. The ranking of RCOs varies depending on the scenarios with different congestion risk conditions. The research findings indicate that some RCOs (e.g. “Channel dredging and maintenance”, and “Prohibition of navigation”) are more cost effective in the situation of a high level of congestion risk, while the other RCOs (e.g. “Loading restriction”, and “Crew management and training”) are more beneficial in a relatively low congestion risk condition. The proposed methods and the evaluation results provide useful insights for effective safety management of the inland waterway congestions under dynamic risk conditions.

      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.05.053
      Issue No: Vol. 59 (2017)
  • Two-phase optimization for support vectors and parameter selection of
           support vector machines: Two-class classification
    • Authors: Shinq-Jen Wu; Van-Hung Pham; Thi-Nga Nguyen
      Pages: 129 - 142
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Shinq-Jen Wu, Van-Hung Pham, Thi-Nga Nguyen
      Support vector machines (SVMs) are one of the most popular classification tools and show the most potential to address under-sampled noisy data (a large number of features and a relatively small number of samples). However, the computational cost is too expensive, even for modern-scale samples, and the performance largely depends on the proper setting of parameters. As the data scale increases, the improvement in speed becomes increasingly challenging. As the dimension (feature number) largely increases while the sample size remains small, the avoidance of overfitting becomes a significant challenge. In this study, we propose a two-phase sequential minimal optimization (TSMO) to largely reduce the training cost for large-scale data (tested with 3186–70,000-sample datasets) and a two-phased-in differential-learning particle swarm optimization (tDPSO) to ensure the accuracy for under-sampled data (tested with 2000–24481-feature datasets). Because the purpose of training SVMs is to identify support vectors that denote a hyperplane, TSMO is developed to quickly select support vector candidates from the entire dataset and then identify support vectors from those candidates. In this manner, the computational burden is largely reduced (a 29.4%–65.3% reduction rate). The proposed tDPSO uses topology variation and differential learning to solve PSO’s premature convergence issue. Population diversity is ensured through dynamic topology until a ring connection is achieved (topology-variation phases). Further, particles initiate chemo-type simulated-annealing operations, and the global-best particle takes a two-turn diversion in response to stagnation (event-induced phases). The proposed tDPSO-embedded SVMs were tested with several under-sampled noisy cancer datasets and showed superior performance over various methods, even those methods with feature selection for the preprocessing of data.
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      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.05.021
      Issue No: Vol. 59 (2017)
  • Adaptive local data and membership based KL divergence incorporating
           C-means algorithm for fuzzy image segmentation
    • Authors: R.R. Gharieb; G. Gendy; A. Abdelfattah; H. Selim
      Pages: 143 - 152
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): R.R. Gharieb, G. Gendy, A. Abdelfattah, H. Selim
      In this paper, a fuzzy clustering technique for image segmentation is developed by incorporating a hybrid of local spatial membership and data information into the conventional hard C-means (HCM) algorithm. This incorporation is a threefold procedure. (1) The membership function of a pixel is spatially smoothed in the pixel vicinity. (2) The Kullback-Leibler (KL) divergence between the pixel membership function and the smoothed one is added to the HCM objective function for fuzzification. (3) The resulting fuzzified HCM is regularized by adding a weighted HCM-like function where the original pixel data are replaced by locally smoothed ones. Thereby the weight is proportional to the residual of the locally smoothed membership. This residual decreases when many pixels existing in the pixel vicinity belong to the same cluster. Thus, the weighted distance decreases, allowing the pixel membership to follow the dominant membership in the pixel vicinity. The simulation results of segmenting synthetic, medical and media images have shown that the proposed algorithm provides better performance compared to several previously developed algorithms. For example, in a synthetic image, with added white Gaussian noise having a variance of 0.3, the proposed algorithm provides accuracy, sensitivity and specificity of 92%, 84% and 94.7% respectively, while the algorithm with the closest results provides 81.9% of accuracy, 62.2% of sensitivity and 86.8% of specificity. In addition, the proposed algorithm shows the capability to identify the number of clusters.
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      PubDate: 2017-07-03T01:38:23Z
      DOI: 10.1016/j.asoc.2017.05.055
      Issue No: Vol. 59 (2017)
  • A group decision-making model based on incomplete comparative expressions
           with hesitant linguistic terms
    • Authors: Yongming Song; Jun Hu
      Pages: 174 - 181
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Yongming Song, Jun Hu
      As a result of uncertainty and complexity for environments of decision-making, it is more suitable for decision makers to use hesitant fuzzy linguistic information. In this paper, a novel group decision making (GDM) model based on fuzzy linear programming is proposed for incomplete comparative expressions with hesitant fuzzy linguistic term set (HFLTSs). We establish an equivalence theorem of additive consistency between 2-tuple fuzzy linguistic preference relation (FLPR) and corresponding fuzzy preference relation. Based on this framework, a fuzzy linear programming is established to address incomplete comparative expressions with HFLTSs. It is more important that the proposed fuzzy linear programming has a double action, finding the highest consistent incomplete 2-tuple FLPR and increasing inconsistent 2-tuple FLPR to the additive consistent 2-tuple FLPR based on given incomplete comparative expressions with HFLTSs. By this means, a novel GDM model is constructed based on importance induced ordered weighted averaging operator. Finally, an investment decision-making in real-world is solved by the proposed model, which shows the result of GDM is effectiveness.

      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.05.056
      Issue No: Vol. 59 (2017)
  • A time-varying transfer function for balancing the exploration and
           exploitation ability of a binary PSO
    • Authors: Md. Jakirul Islam; Xiaodong Li; Yi Mei
      Pages: 182 - 196
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Md. Jakirul Islam, Xiaodong Li, Yi Mei
      Many real-world problems belong to the family of discrete optimization problems. Most of these problems are NP-hard and difficult to solve efficiently using classical linear and convex optimization methods. In addition, the computational difficulties of these optimization tasks increase rapidly with the increasing number of decision variables. A further difficulty can be also caused by the search space being intrinsically multimodal and non-convex. In such a case, it is more desirable to have an effective optimization method that can cope better with these problem characteristics. Binary particle swarm optimization (BPSO) is a simple and effective discrete optimization method. The original BPSO and its variants have been used to solve a number of classic discrete optimization problems. However, it is reported that the original BPSO and its variants are unable to provide satisfactory results due to the use of inappropriate transfer functions. More specifically, these transfer functions are unable to provide BPSO a good balance between exploration and exploitation in the search space, limiting their performances. To overcome this problem, this paper proposes to employ a time-varying transfer function in the BPSO, namely TV T -BPSO. To understand the search behaviour of the TV T -BPSO, we provide a systematic analysis of its exploration and exploitation capability. Our experimental results demonstrate that TV T -BPSO outperforms existing BPSO variants on both low-dimensional and high-dimensional classical 0–1 knapsack problems, as well as a 200-member truss problem, suggesting that TV T -BPSO is able to better scale to high dimensional combinatorial problems than the existing BPSO variants and other metaheuristic algorithms.
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      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.04.050
      Issue No: Vol. 59 (2017)
  • A feature dependent Naive Bayes approach and its application to the
           software defect prediction problem
    • Authors: Ömer Faruk Arar; Kürşat Ayan
      Pages: 197 - 209
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Ömer Faruk Arar, Kürşat Ayan
      Naive Bayes is one of the most widely used algorithms in classification problems because of its simplicity, effectiveness, and robustness. It is suitable for many learning scenarios, such as image classification, fraud detection, web mining, and text classification. Naive Bayes is a probabilistic approach based on assumptions that features are independent of each other and that their weights are equally important. However, in practice, features may be interrelated. In that case, such assumptions may cause a dramatic decrease in performance. In this study, by following preprocessing steps, a Feature Dependent Naive Bayes (FDNB) classification method is proposed. Features are included for calculation as pairs to create dependence between one another. This method was applied to the software defect prediction problem and experiments were carried out using widely recognized NASA PROMISE data sets. The obtained results show that this new method is more successful than the standard Naive Bayes approach and that it has a competitive performance with other feature-weighting techniques. A further aim of this study is to demonstrate that to be reliable, a learning model must be constructed by using only training data, as otherwise misleading results arise from the use of the entire data set.
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      PubDate: 2017-07-24T03:47:33Z
      DOI: 10.1016/j.asoc.2017.05.043
      Issue No: Vol. 59 (2017)
  • Recognition of power quality disturbances using S-transform based ruled
           decision tree and fuzzy C-means clustering classifiers
    • Authors: Om Prakash Mahela; Abdul Gafoor Shaik
      Pages: 243 - 257
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Om Prakash Mahela, Abdul Gafoor Shaik
      A method based on Stockwell's transform and Fuzzy C-means (FCM) clustering initialized by decision tree has been proposed in this paper for detection and classification of power quality (PQ) disturbances. Performance of this method is compared with S-transform based ruled decision tree. PQ disturbances are simulated in conformity with standard IEEE-1159 using MATLAB software. Different statistical features of PQ disturbance signals are obtained using Stockwell's transform based multi-resolution analysis of signals. These features are given as input to the proposed techniques such as rule-based decision tree and FCM clustering initialized by ruled decision tree for classification of various PQ disturbances. The PQ disturbances investigated in this study include voltage swell, voltage sag, interruption, notch, harmonics, spike, flicker, impulsive transient and oscillatory transient. It has been observed that the efficiency of classification based on ruled decision tree deteriorates in the presence of noise. However, the classification based on Fuzzy C-means clustering initialized by decision tree gives results with high accuracy even in the noisy environment. Validity of simulation results has been verified through comparisons with results in real time obtained using the Real Time Digital Simulator (RTDS) in hardware synchronization mode. The proposed algorithm is established effectively by results of high accuracy to detect and classify various electrical power quality disturbances.
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      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.05.061
      Issue No: Vol. 59 (2017)
  • A parallel double-level multiobjective evolutionary algorithm for robust
    • Authors: Wei-Jie Yu; Jin-Zhou Li; Wei-Neng Chen; Jun Zhang
      Pages: 258 - 275
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Wei-Jie Yu, Jin-Zhou Li, Wei-Neng Chen, Jun Zhang
      Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.

      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.06.008
      Issue No: Vol. 59 (2017)
  • A jumping genes inspired multi-objective differential evolution algorithm
           for microwave components optimization problems
    • Authors: Shao Yong Zheng; Sheng Xin Zhang
      Pages: 276 - 287
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Shao Yong Zheng, Sheng Xin Zhang
      Exploitation and exploration are equally important for multi-objective evolutionary algorithms (MOEAs) to approximate the optimal Pareto Front (PF). However, most existing multi-objective differential evolution algorithms (MODEs) focus on exploitation with proposals of elitism methods that may result in poor diversity or sticking into local optimal front. Inspired by a biological discovery, this study proposes a jumping genes based MODE algorithm, termed as JGDE with two components. The first component is the application of jumping genes operator to MODE to promote population diversity while in the second component, an elitism leading mechanism is designed to accelerate the convergence. Experimental studies show the superiority of JGDE over other competitive algorithms in both convergence and diversity. More importantly, JGDE can deal with local optimal fronts that are difficult to handle by the existing MODEs. The studies are also further verified by the solvement of a practical microwave components optimization problem and the comparison between different optimization algorithms.
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      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.05.062
      Issue No: Vol. 59 (2017)
  • Study on an improved adaptive PSO algorithm for solving multi-objective
           gate assignment
    • Authors: Wu Deng; Huimin Zhao; Xinhua Yang; Juxia Xiong; Meng Sun; Bo Li
      Pages: 288 - 302
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Wu Deng, Huimin Zhao, Xinhua Yang, Juxia Xiong, Meng Sun, Bo Li
      Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the effective connection between flights and improve the capacity and service efficiency of airport. The minimum walking distances of passengers, the minimum idle time variance of each gate, the minimum number of flights at parking apron and the most reasonable utilization of large gates are selected as the optimization objectives, then an efficient multi-objective optimization model of gate assignment problem is proposed in this paper. Then an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus is deeply studied. The dynamic fractional calculus with memory characteristic is used to reflect the trajectory information of particle updating in order to improve the convergence speed. The Alpha-stable distribution theory is used to replace the uniform distribution in order to escape from the local minima in a certain probability and improve the global search ability. Next, the DOADAPO algorithm is used to solve the constructed multi-objective optimization model of gate assignment in order to fast and effectively assign the gates to different flights in different time. Finally, the actual flight data in one domestic airport is used to verify the effectiveness of the proposed method. The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improve the comprehensive service of gate assignment. It can effectively provide a valuable reference for assigning the gates in hub airport.
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      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.06.004
      Issue No: Vol. 59 (2017)
  • A hybrid EDA for load balancing in multicast with network coding
    • Authors: Huanlai Xing; Saifei Li; Yunhe Cui; Lianshan Yan; Wei Pan; Rong Qu
      Pages: 363 - 377
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Huanlai Xing, Saifei Li, Yunhe Cui, Lianshan Yan, Wei Pan, Rong Qu
      Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms.

      PubDate: 2017-06-21T14:33:54Z
      DOI: 10.1016/j.asoc.2017.06.003
      Issue No: Vol. 59 (2017)
  • UFuzzy: Fuzzy models with Universum
    • Authors: L. Tencer; M. Reznakova; M. Cheriet
      Pages: 1296 - 1315
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): L. Tencer, M. Reznakova, M. Cheriet
      Recently, many works have begun to explore the possibility of using samples out of the training set to improve their results. One of these approaches, Universum, introduced by Vapnik had already been used in combination with several classifiers to increase their accuracy. In this paper, we present a novel approach for identifying the consequent parameters of the Takagi-Sugeno Fuzzy Model, U Fuzzy. It is based on the idea of the Universum set, which acts to regularize the optimization problem. It also helps with the introduction of prior knowledge to the tasks performed by the model. In addition, we explore the influence of the Universum set on identifying the structure of fuzzy rules using the c-means clustering algorithm. We evaluated our approach on several generated and real-world classification datasets and it shows promising results in comparison to the baseline methods, which do not use the Universum set.

      PubDate: 2017-06-02T02:21:18Z
      DOI: 10.1016/j.asoc.2016.05.041
      Issue No: Vol. 52 (2017)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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