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  Subjects -> COMPUTER SCIENCE (Total: 1991 journals)
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    - COMPUTER SCIENCE (1157 journals)
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    - INTERNET (92 journals)
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    - THEORY OF COMPUTING (8 journals)

COMPUTER SCIENCE (1157 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: 13)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
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: 12)
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: 14)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 5)
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: 21)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 3)
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: 9)
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: 25)
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: 8)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
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: 14)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 51)
Advances in Engineering Software     Hybrid Journal   (Followers: 26)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 26)
Advances in Human-Computer Interaction     Open Access   (Followers: 20)
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: 38)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 2)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 8)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 9)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 7)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 7)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 14)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
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: 33)
Applied Medical Informatics     Open Access   (Followers: 11)
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: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 133)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 6)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 311)
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: 44)
British Journal of Educational Technology     Hybrid Journal   (Followers: 129)
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: 14)
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: 12)
Communication Theory     Hybrid Journal   (Followers: 20)
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: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 14)
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: 31)
Computer     Full-text available via subscription   (Followers: 87)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 7)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 8)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 21)
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: 16)
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: 12)
Computer Science Education     Hybrid Journal   (Followers: 13)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 10)
Computer Science Review     Hybrid Journal   (Followers: 10)

        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  [3048 journals]
  • Hybrid Differential Evolution and Greedy Algorithm (DEGR) for solving
           Multi-Skill Resource-Constrained Project Scheduling Problem
    • Authors: Paweł B. Myszkowski; Łukasz P. Olech; Maciej Laszczyk; Marek E. Skowroński
      Pages: 1 - 14
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Paweł B. Myszkowski, Łukasz P. Olech, Maciej Laszczyk, Marek E. Skowroński
      Paper presents a hybrid Differential Evolution and Greedy Algorithm (DEGR) applied to solve Multi-Skill Resource-Constrained Project Scheduling Problem. The specialized indirect representation and transformation of solution space from discrete (typical for this problem), to continuous (typical for DE-approaches) are proposed and examined. Furthermore, Taguchi Design of Experiments method has been used to adjust parameters for investigated method to reduce the procedure of experiments. Finally, various initialisation, clone elimination, mutation and crossover operators have been applied there. The results have been compared with the results from other reference methods (HantCO, GRASP and multiStart Greedy) using the benchmark iMOPSE dataset. This comparison shows that DEGR effort is very robust and effective. For 28 instances of iMOPSE dataset DEGR has achieved the best-known solutions.
      Graphical abstract image

      PubDate: 2017-11-02T14:01:33Z
      DOI: 10.1016/j.asoc.2017.10.014
      Issue No: Vol. 62 (2017)
       
  • Hybrid techniques based on solving reduced problem instances for a longest
           common subsequence problem
    • Authors: Christian Blum; Maria J. Blesa
      Pages: 15 - 28
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Christian Blum, Maria J. Blesa
      Finding the longest common subsequence of a given set of input strings is a relevant problem arising in various practical settings. One of these problems is the so-called longest arc-preserving common subsequence problem. This NP-hard combinatorial optimization problem was introduced for the comparison of arc-annotated ribonucleic acid (RNA) sequences. In this work we present an integer linear programming (ILP) formulation of the problem. As even in the context of rather small problem instances the application of a general purpose ILP solver is not viable due to the size of the model, we study alternative ways based on model reduction in order to take profit from this ILP model. First, we present a heuristic way for reducing the model, with the subsequent application of an ILP solver. Second, we propose the application of an iterative hybrid algorithm that makes use of an ILP solver for generating high quality solutions at each iteration. Experimental results concerning artificial and real problem instances show that the proposed techniques outperform an available technique from the literature.

      PubDate: 2017-11-02T14:01:33Z
      DOI: 10.1016/j.asoc.2017.10.005
      Issue No: Vol. 62 (2017)
       
  • A modified firefly algorithm for global minimum optimization
    • Authors: Aref Yelghi; Cemal Köse
      Pages: 29 - 44
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Aref Yelghi, Cemal Köse
      The Firefly algorithm is a population-based optimization algorithm. It has become popular in the field of optimization and has been applied to engineering practices. Recent works have failed to address how to find the global minimum because their algorithm was trapped in the local minimum. Also, they were not able to provide a balance between exploration and exploitation. In this paper, the Tidal Force formula has been applied to modify the Firefly algorithm, which describes the effect of a massive body that gravitationally affects another massive body. The proposed algorithm brings a new strategy into the optimization field. It is applied by using exploitation (Tidal Force) and keeping a balance between the exploration and exploitation on function suitability. Plate shaped, Steep Ridges, Unimodal and Multimodal benchmark functions were used to compare experimental results. The study findings indicate that the Tidal Force Firefly algorithm outperforms the other existing modified Firefly algorithms.

      PubDate: 2017-11-02T14:01:33Z
      DOI: 10.1016/j.asoc.2017.10.032
      Issue No: Vol. 62 (2017)
       
  • MRCCA: A novel CCA based method and its application in feature extraction
           and fusion for matrix data
    • Authors: Xizhan Gao; Quansen Sun; Jing Yang
      Pages: 45 - 56
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Xizhan Gao, Quansen Sun, Jing Yang
      Multiset features extracted from the same pattern usually represent different characteristics of data, meanwhile, matrices or 2-order tensors are common forms of data in real applications. Hence, how to extract multiset features from matrix data is an important research topic for pattern recognition. In this paper, by analyzing the relationship between CCA and 2D-CCA, a novel feature extraction method called multiple rank canonical correlation analysis (MRCCA) is proposed, which is an extension of 2D-CCA. Different from CCA and 2D-CCA, in MRCCA k pairs left transforms and k pairs right transforms are sought to maximize correlation. Besides, the multiset version of MRCCA termed as multiple rank multiset canonical correlation analysis (MRMCCA) is also developed. Experimental results on five real-world data sets demonstrate the viability of the formulation, they also show that the recognition rate of our method is higher than other methods and the computing time is competitive.
      Graphical abstract image

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.008
      Issue No: Vol. 62 (2017)
       
  • A dual fast and slow feature interaction in biologically inspired visual
           recognition of human action
    • Authors: Bardia Yousefi; Chu Kiong Loo
      Pages: 57 - 72
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Bardia Yousefi, Chu Kiong Loo
      Computational neuroscience studies have examined the human visual system through functional magnetic resonance imaging (fMRI) and identified a model where the mammalian brain pursues two independent pathways for recognizing biological movement tasks. On the one hand, the dorsal stream analyzes the motion information by applying optical flow, which considers the fast features. On the other hand, the ventral stream analyzes the form information with slow features. The proposed approach suggests that the motion perception of the human visual system comprises fast and slow feature interactions to identify biological movements. The form features in the visual system follow the application of the active basis model (ABM) with incremental slow feature analysis (IncSFA). Episodic observation is required to extract the slowest features, whereas the fast features update the processing of motion information in every frame. Applying IncSFA provides an opportunity to abstract human actions and use action prototypes. However, the fast features are obtained from the optical flow division, which gives an opportunity to interact with the system as the final recognition is performed through a combination of the optical flow and ABM-IncSFA information and through the application of kernel extreme learning machine. Applying IncSFA into the ventral stream and involving slow and fast features in the recognition mechanism are the major contributions of this research. The two human action datasets for benchmarking (KTH and Weizmann) and the results highlight the promising performance of this approach in model modification.
      Graphical abstract image

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.021
      Issue No: Vol. 62 (2017)
       
  • Predicting corporate investment/non-investment grade by using
           interval-valued fuzzy rule-based systems—A cross-region analysis
    • Authors: Petr Hajek
      Pages: 73 - 85
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Petr Hajek
      Systems for predicting corporate rating have attracted considerable interest in soft computing research due to the requirements for both accuracy and interpretability. In addition, the high uncertainty associated primarily with linguistic uncertainties and disagreement among experts is another challenging problem. To overcome these problems, this study proposes a hybrid evolutionary interval-valued fuzzy rule-based system, namely IVTURS, combined with evolutionary feature selection component. This model is used to predict the investment/non-investment grades of companies from four regions, namely Emerging countries, the EU, the United States, and other developed countries. To evaluate prediction performance, a yield measure is used that combines the return and default rates of companies. Here, we show that using interval-valued fuzzy sets leads to higher accuracy, particularly with the growing granularity at the fuzzy partition level. The proposed prediction model is then compared with several state-of-the-art evolutionary fuzzy rule-based systems. The obtained results show that the proposed model is especially suitable for high-dimensional problems, without facing rule base interpretability issues. This finding indicates that the model is preferable for investors oriented toward developed markets such as the EU and the United States.
      Graphical abstract image

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.037
      Issue No: Vol. 62 (2017)
       
  • Modeling of a greenhouse prototype using PSO and differential evolution
           algorithms based on a real-time LabView™ application
    • Authors: Alfonso Pérez-González; Ofelia Begovich-Mendoza; Javier Ruiz-León
      Pages: 86 - 100
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Alfonso Pérez-González, Ofelia Begovich-Mendoza, Javier Ruiz-León
      In general, to obtain an adequate mathematical model of a greenhouse is a difficult task due to the complexity of the involved equations that describe the dynamics of the system, and the required high number of physical parameters, which are complicated or even impossible to measure. In these situations, estimation methods are commonly used to obtain a suitable approximation for those parameters. This paper presents the application and comparison of a collection of methods based on Particle Swarm Optimization (PSO) and Differential Evolution (DE), using them as the tools to identify the parameters that complete a proposed mathematical model for a greenhouse. These parameters are sought aiming to approximate the dynamic behavior of a greenhouse physical prototype building in CINVESTAV Campus Guadalajara, by using the heuristic algorithms in order to minimize a proposed error function, which considers as arguments estimations and measurements of the two more representative dynamics of the climate conditions inside a greenhouse: namely, the air temperature and relative humidity. Different forms of PSO and DE algorithms are considered and applied in order to select the one that achieves the set of parameters with the lowest evaluation error. The comparison of the selected algorithms is carried out in an offline optimization schedule using real data recorded through the LabView™ SignalExpress application, and a real-time implementation in a LabView™ code, implemented to optimize the model in a sample to sample execution. The proposed model, with its corresponding computed parameters, is validated comparing its results against the real dynamic behavior of the temperature and relative humidity, that are measured directly from the greenhouse prototype, showing a good agreement between real and estimated values. Several tests were executed in order to find PSO and DE best calibration conditions. Experimental results allow us to propose an efficient way to deal with numerical optimization problems of high complexity, applying a two stages scheme based on a first offline pre-identification, where the obtained results are used as initial condition for an online, real-time refinement process.
      Graphical abstract image

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.023
      Issue No: Vol. 62 (2017)
       
  • Application of improved bat algorithm for solar PV maximum power point
           tracking under partially shaded condition
    • Authors: Zhongqiang Wu; Danqi Yu
      Pages: 101 - 109
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Zhongqiang Wu, Danqi Yu
      The power output curves of solar photovoltaic (PV) system have multiple peaks under partially shaded condition. As the same as traditional MPPT (Maximum Power Point Tracking) search methods, bat algorithm often makes optimized results fall into local extremum. So an improved bat algorithm is proposed. Chaos search strategy is introduced in initial arrangement to improve the uniformity and ergodicity of population. Adapting weight is introduced to balance the global searching ability and the local searching ability. Dynamic contraction regain decreases the search range more effectively. Compared with the original algorithm, the rapidity and accuracy of algorithm have been improved. The simulation shows that improved bat algorithm can find the globally optimal point fast, with high precision, under the partially shaded condition.
      Graphical abstract image

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.039
      Issue No: Vol. 62 (2017)
       
  • Artifical bee colony algorithm using problem-specific neighborhood
           strategies for the tree t-spanner problem
    • Authors: Kavita Singh; Shyam Sundar
      Pages: 110 - 118
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Kavita Singh, Shyam Sundar
      A tree t-spanner is a spanning tree T in which the ratio of distance between every pair of vertices is at most t times their shortest distance in a connected graph, where t is a value called stretch factor of T. On a given connected, undirected, and weighted graph, this paper studies the tree t-spanner problem (Tree_t-SP) that aims to find a spanning tree whose stretch factor is minimum amongst all spanning trees of the graph. Being a N P -Hard for any fixed t >1, this problem is under-studied in the domain of metaheuristic techniques. In literature, only genetic algorithm has been proposed for this problem. This paper presents an artificial bee colony (ABC) algorithm for this problem, where ABC algorithm is a swarm intelligence technique inspired by intelligent foraging behavior of honey bees. Neighborhood strategies of ABC algorithm particularly employ problem-specific knowledge that makes ABC algorithm highly effective in searching high quality solutions in less computational time. Computational experiments on a large set of randomly generated graph instances exhibit superior performance of ABC algorithm over the existing genetic algorithm for the Tree_t-SP.
      Graphical abstract image

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.022
      Issue No: Vol. 62 (2017)
       
  • DetectA: abrupt concept drift detection in non-stationary environments
    • Authors: Tatiana Escovedo; Adriano Koshiyama; Andre Abs da Cruz; Marley Vellasco
      Pages: 119 - 133
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Tatiana Escovedo, Adriano Koshiyama, Andre Abs da Cruz, Marley Vellasco
      Almost all drift detection mechanisms designed for classification problems work reactively: after receiving the complete data set (input patterns and class labels) they apply a sequence of procedures to identify some change in the class-conditional distribution – a concept drift. However, detecting changes after its occurrence can be in some situations harmful to the process under analysis. This paper proposes a proactive approach for abrupt drift detection, called DetectA (Detect Abrupt Drift). Briefly, this method is composed of three steps: (i) label the patterns from the test set (an unlabelled data block), using an unsupervised method; (ii) compute some statistics from the train and test sets, conditioned to the given class labels for train set; and (iii) compare the training and testing statistics using a multivariate hypothesis test. Based on the results of the hypothesis tests, we attempt to detect the drift on the test set, before the real labels are obtained. A procedure for creating datasets with abrupt drift has been proposed to perform a sensitivity analysis of the DetectA model. The result of the sensitivity analysis suggests that the detector is efficient and suitable for datasets of high-dimensionality, blocks with any proportion of drifts, and datasets with class imbalance. The performance of the DetectA method, with different configurations, was also evaluated on real and artificial datasets, using an MLP as a classifier. The best results were obtained using one of the detection methods, being the proactive manner a top contender regarding improving the underlying base classifier accuracy.

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.031
      Issue No: Vol. 62 (2017)
       
  • Semi-supervised topo-Bayesian ARTMAP for noisy data
    • Authors: Parham Nooralishahi; Chu Kiong Loo; Manjeevan Seera
      Pages: 134 - 147
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Parham Nooralishahi, Chu Kiong Loo, Manjeevan Seera
      This paper presents a novel semi-supervised ART network that inherits the ability of noise insensitivity, topology learning, and incremental learning from the Bayesian ARTMAP. It is combined with a label prediction strategy based on a clustering technique to determine the neighboring neurons. The procedure of updating Bayesian ARTMAP is modified to allow the network in altering the learning rate. This results in a classifier that works online and lifts several limitations of the original Bayesian ARTMAP. It processes arbitrarily scaled values even when their range is not entirely known in advance. The classifier has the capability to be employed in online learning applications, in which no prior-knowledge about the structure and distribution of data is available. Experimental results indicate good results, even with noisy data.

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.011
      Issue No: Vol. 62 (2017)
       
  • A meta optimisation analysis of particle swarm optimisation velocity
           update equations for watershed management learning
    • Authors: Karl Mason; Jim Duggan; Enda Howley
      Pages: 148 - 161
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Karl Mason, Jim Duggan, Enda Howley
      Particle swarm optimisation (PSO) is a general purpose optimisation algorithm used to address hard optimisation problems. The algorithm operates as a result of a number of particles converging on what is hoped to be the best solution. How the particles move through the problem space is therefore critical to the success of the algorithm. This study utilises meta optimisation to compare a number of velocity update equations to determine which features of each are of benefit to the algorithm. A number of hybrid velocity update equations are proposed based on other high performing velocity update equations. This research also presents a novel application of PSO to train a neural network function approximator to address the watershed management problem. It is found that the standard PSO with a linearly changing inertia, the proposed hybrid Attractive Repulsive PSO with avoidance of worst locations (AR PSOAWL) and Adaptive Velocity PSO (AV PSO) provide the best performance overall. The results presented in this paper also reveal that commonly used PSO parameters do not provide the best performance. Increasing and negative inertia values were found to perform better.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.018
      Issue No: Vol. 62 (2017)
       
  • A hyper-heuristic approach to automated generation of mutation operators
           for evolutionary programming
    • Authors: Libin Hong; John H. Drake; John R. Woodward; Ender Özcan
      Pages: 162 - 175
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Libin Hong, John H. Drake, John R. Woodward, Ender Özcan
      Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Lévy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest.

      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.002
      Issue No: Vol. 62 (2017)
       
  • IntensityPatches and RegionPatches for image recognition
    • Authors: Tiago B.A. de Carvalho; Maria A.A. Sibaldo; Ing Ren Tsang; George D.C. Cavalcanti; Jan Sijbers; Ing Jyh Tsang
      Pages: 176 - 186
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Tiago B.A. de Carvalho, Maria A.A. Sibaldo, Ing Ren Tsang, George D.C. Cavalcanti, Jan Sijbers, Ing Jyh Tsang
      In this paper, we propose a framework for defining feature extraction techniques, called Pixel Clustering. It is an extension of feature extraction with Wavelets. We propose two linear feature extraction techniques using Pixel Clustering: IntensityPatches and RegionPatches. We assess the methods in color and grayscale image datasets: two face datasets and two object datasets. The proposed methods present a short computation time for feature extraction and high accuracy compared with linear feature extraction methods and other state-of-the-art feature extraction techniques.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.09.046
      Issue No: Vol. 62 (2017)
       
  • Non-dominated sorting biogeography-based optimization for bi-objective
           reentrant flexible manufacturing system scheduling
    • Authors: Achmad P. Rifai; Huu-Tho Nguyen; Hideki Aoyama; Siti Zawiah Md Dawal; Nur Aini Masruroh
      Pages: 187 - 202
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Achmad P. Rifai, Huu-Tho Nguyen, Hideki Aoyama, Siti Zawiah Md Dawal, Nur Aini Masruroh
      Scheduling in flexible manufacturing systems (FMS) is described as an NP-Hard problem. Its complexity has increased significantly in line with the development of FMS over the past years. This paper presents a non-dominated sorting biogeography-based optimization (NSBBO) for scheduling problem of FMS having multi loading-unloading and shortcuts infused in the reentrant characteristics. This model is formulated to identify the near optimal trade-off solutions capable of addressing the bi-objectives of minimization of makespan and total earliness. The goal is to simultaneously determine the best machine assignment and job sequencing to satisfy both objectives. We propose the development of NSBBO by substituting the standard linear function of emigration-immigration rate with three approaches based on sinusoidal, quadratic and trapezoidal models. A selection of test problems was examined to analyze the effectiveness, efficiency and diversity levels of the proposed approaches as compared to standard NSBBO and NSGA-II. The results have shown that the NSBBO-trapezoidal model performed favorably and is comparable to current existing models. We conclude that the developed NSBBO and its variants are suitable alternative methods to achieve the bi-objective satisfaction of reentrant FMS scheduling problem.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.045
      Issue No: Vol. 62 (2017)
       
  • Correlation feature selection based improved-Binary Particle Swarm
           Optimization for gene selection and cancer classification
    • Authors: Indu Jain; Vinod Kumar Jain; Renu Jain
      Pages: 203 - 215
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Indu Jain, Vinod Kumar Jain, Renu Jain
      DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and its classification. It provides better insights of many genetic mutations occurring within a cell associated with cancer. However, thousands of gene expressions measured for each biological sample using microarray pose a great challenge. Many statistical and machine learning methods have been applied to get most relevant genes prior to cancer classification. A two phase hybrid model for cancer classification is being proposed, integrating Correlation-based Feature Selection (CFS) with improved-Binary Particle Swarm Optimization (iBPSO). This model selects a low dimensional set of prognostic genes to classify biological samples of binary and multi class cancers using Naive–Bayes classifier with stratified 10-fold cross-validation. The proposed iBPSO also controls the problem of early convergence to the local optimum of traditional BPSO. The proposed model has been evaluated on 11 benchmark microarray datasets of different cancer types. Experimental results are compared with seven other well known methods, and our model exhibited better results in terms of classification accuracy and the number of selected genes in most cases. In particular, it achieved up to 100% classification accuracy for seven out of eleven datasets with a very small sized prognostic gene subset (up to <1.5%) for all eleven datasets.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.09.038
      Issue No: Vol. 62 (2017)
       
  • New optimal controller tuning method for an AVR system using a simplified
           Ant Colony Optimization with a new constrained Nelder–Mead algorithm
    • Authors: M.J. Blondin; J. Sanchis; P. Sicard; J.M. Herrero
      Pages: 216 - 229
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): M.J. Blondin, J. Sanchis, P. Sicard, J.M. Herrero
      In this paper, an optimal gain tuning method for PID controllers is proposed using a novel combination of a simplified Ant Colony Optimization algorithm and Nelder–Mead method (ACO-NM) including a new procedure to constrain NM. To address Proportional-Integral-Derivative (PID) controller tuning for the Automatic Voltage Regulator (AVR) system, this paper presents a meta-analysis of the literature on PID parameter sets solving the AVR problem. The investigation confirms that the proposed ACO-NM obtains better or equivalent PID solutions and exhibits higher computational efficiency than previously published methods. The proposed ACO-NM application is extended to realistic conditions by considering robustness to AVR process parameters, control signal saturation and noisy measurements as well as tuning a two-degree-of-freedom PID controller (2DOF-PID). For this type of PID, a new objective function is also proposed to manage control signal constraints. Finally, real time control experiments confirm the performance of the proposed 2DOF-PIDs in quasi-real conditions. Furthermore, the efficiency of the algorithm is confirmed by comparing its results to other optimization algorithms and NM combinations using benchmark functions.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.10.007
      Issue No: Vol. 62 (2017)
       
  • Bi-stage hierarchical selection of pathway genes for cancer progression
           using a swarm based computational approach
    • Authors: Prativa Agarwalla; Sumitra Mukhopadhyay
      Pages: 230 - 250
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Prativa Agarwalla, Sumitra Mukhopadhyay
      Background Understanding of molecular mechanism, lying beneath the carcinogenic expression, is very essential for early and accurate detection of the disease. It predicts various types of irregularities and results in effective drug selection for the treatment. Pathway information plays an important role in mapping of genotype information to phenotype parameters. It helps to find co-regulated gene groups whose collective expression is strongly associated with the cancer development. Method In this paper, we have proposed a bi-stage hierarchical swarm based gene selection technique which combines two methods, proposed in this paper for the first time. First one is a multi-fitness discrete particle swarm optimization (MFDPSO) based feature selection procedure, having multiple fitness functions. This technique uses multi-filtering based gene selection procedure. On top of it, a new blended Laplacian artificial bee colony algorithm (BLABC) is proposed and it is used for automatic clustering of the selected genes obtained from the first procedure. We have performed 10 times 10-fold cross validation and compared our proposed method with various statistical and swarm based gene selection techniques for different popular cancer datasets. Result Experimental results show that the proposed method as a whole performs significantly well. The MFDPSO based system in combination with BLABC generates a good subset of pathway markers which provides more effective insight into the gene-disease association with high accuracy and reliability.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.024
      Issue No: Vol. 62 (2017)
       
  • Deep neural network in QSAR studies using deep belief network
    • Authors: Fahimeh Ghasemi; Alireza Mehridehnavi; Afshin Fassihi; Horacio Pérez-Sánchez
      Pages: 251 - 258
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Fahimeh Ghasemi, Alireza Mehridehnavi, Afshin Fassihi, Horacio Pérez-Sánchez
      There are two major challenges in the current high throughput screening drug design: the large number of descriptors which may also have autocorrelations and, proper parameter initialization in model prediction to avoid over-fitting problem. Deep architecture structures have been recommended to predict the compounds biological activity. Performance of deep neural network is not always acceptable in QSAR studies. This study tries to find a solution to this problem focusing on primary parameter computation. Deep belief network has been getting popular as a deep neural network model generation method in other fields such as image processing. In the current study, deep belief network is exploited to initialize deep neural networks. All fifteen targets of Kaggle data sets containing more than 70k molecules have been utilized to investigate the model performance. The results revealed that an optimization in parameter initialization will improve the ability of deep neural networks to provide high quality model predictions. The mean and variance of squared correlation for the proposed model and deep neural network are 0.618±0.407e−4 and 0.485±4.82e−4, respectively. The outputs of this model seem to outperform those of the models obtained from deep neural network.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.09.040
      Issue No: Vol. 62 (2017)
       
  • A hybrid local-search algorithm for robust job-shop scheduling under
           scenarios
    • Authors: Bing Wang; Xiaozhi Wang; Fengming Lan; Quanke Pan
      Pages: 259 - 271
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Bing Wang, Xiaozhi Wang, Fengming Lan, Quanke Pan
      This paper discusses an uncertain job-shop scheduling problem with the makespan as the performance criterion. Uncertain processing times are described by discrete scenarios. A robust optimization model is established for the job-shop scheduling problem based on a set of bad scenarios to hedge against the risk of achieving substandard performances among these bad scenarios. To solve the established problem, a problem-specific neighborhood structure is constructed by uniting multiple single-scenario neighborhoods. The constructed neighborhood structure is applied in a hybrid local-search algorithm of combining the simulated-annealing search and the tabu technique. An extensive computational experiment was conducted. The developed algorithm was compared with two possible alternative algorithms. The computational results show the efficiency of the defined neighborhood structure and the competitiveness of the developed hybrid local-search algorithm for the established model.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.020
      Issue No: Vol. 62 (2017)
       
  • An iterative solution approach to a multi-objective facility location
           problem
    • Authors: Mumtaz Karatas; Ertan Yakıcı
      Pages: 272 - 287
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Mumtaz Karatas, Ertan Yakıcı
      This work presents a novel methodology for solving multi-objective facility location problems (mo-FLPs) with the focus on public emergency service stations. Our study is one of a few studies incorporating the objectives of three well-known problems, viz. the p-median problem (pMP), the maximal coverage location problem (MCLP) and the p-center problem (pCP). Aiming to find a set of Pareto optimal solutions and a compromise solution for all three objectives, we have developed an algorithm which solves each individual location problem sequentially. The proposed approach is mainly based on a combination of the branch & bound and iterative goal programming techniques. The performance of the algorithm is demonstrated with numerical examples.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.10.035
      Issue No: Vol. 62 (2017)
       
  • Multi-Objective Individualized-Instruction Teaching-Learning-Based
           Optimization Algorithm
    • Authors: Dong Yu; Jun Hong; Jinhua Zhang; Qingbo Niu
      Pages: 288 - 314
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Dong Yu, Jun Hong, Jinhua Zhang, Qingbo Niu
      Traditional multi-objective evolutionary algorithms (MOEAs) adopt selection and reproduction operators to find approximate solutions for multi-objective optimization problems (MOPs). The Pareto-dominance-based method is an important branch of MOEA research which exploits dominance relations information. To use dominance relations information more efficiently, this paper proposes an individualized instruction mechanism combined with the non-dominated sorting concept and the teaching-learning process of teaching-learning-based optimization (TLBO). This algorithm, with its individualized instruction mechanism (INM-TLBO), places greater emphasis on the guiding role of the non-dominated solution. INM-TLBO designates specific teachers or interactive objects to help learners improve in the individualized teaching-learning process and adopts an external archive to preserve the best solution found. In addition, the INM-TLBO needs only generic control parameters as input, such as population size, an epsilon value for the external archive, and a stop criterion (maximal generation or function evaluation). The performance of INM-TLBO was evaluated on three test problem sets, including twelve extensively used unconstrained test problems, six truly disconnected test problems, and ten complex continuous unconstrained optimization test problems originally proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The numerical results are compared with those of other state-of-the-art algorithms and show that INM-TLBO has good convergence and high robustness on these test problems.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.08.056
      Issue No: Vol. 62 (2017)
       
  • Evaluation of a novel fuzzy sequential pattern recognition tool (fuzzy
           elastic matching machine) and its applications in speech and handwriting
           recognition
    • Authors: Sina Shahmoradi; Saeed Bagheri Shouraki
      Pages: 315 - 327
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Sina Shahmoradi, Saeed Bagheri Shouraki
      Sequential pattern recognition has long been an important topic of soft computing research with a wide area of applications including speech and handwriting recognition. In this paper, the performance of a novel fuzzy sequential pattern recognition tool named “Fuzzy Elastic Matching Machine” has been investigated. This tool overcomes the shortcomings of the HMM including its inflexible mathematical structure and inconsistent mathematical assumptions with imprecise input data. To do so, “Fuzzy Elastic Pattern” was introduced as the basic element of FEMM. It models the elasticity property of input data using fuzzy vectors. A sequential pattern such as a word in speech or a piece of writing is treated as a sequence of parts in which each part has an elastic nature (i.e. can skew or stretch depending on the speaker/writer's style). To present FEMM as a sequential pattern recognition tool, three basic problems, including evaluation, assignment, and training problems, were defined and their solutions were presented for FEMMs. Finally, we implemented FEMM for speech and handwriting recognition on some large databases including TIMIT database and Dr. Kabir's Persian handwriting database. In speech recognition, FEMM achieved 71% and 75.5% recognition rates in phone and word recognition, respectively. Also, 75.9% recognition accuracy was obtained in Persian handwriting recognition. The results indicated 18.2% higher recognition speed and 9–16% more immunity to noise in speech recognition in addition to 5% higher recognition rate in handwriting recognition compared to the HMM.
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      PubDate: 2017-11-08T14:06:51Z
      DOI: 10.1016/j.asoc.2017.10.036
      Issue No: Vol. 62 (2017)
       
  • A tri-level location-allocation model for forward/reverse supply chain
    • Authors: Amir Mohammad Fathollahi Fard; Mostafa Hajaghaei-Keshteli
      Pages: 328 - 346
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Amir Mohammad Fathollahi Fard, Mostafa Hajaghaei-Keshteli
      The design of supply chain network usually directly influences the performance of location-allocation of facilities, especially for the main parties. This paper firstly addresses the tri-level location-allocation design problem which considers the forward and reverse network, simultaneously. The proposed problem is formulated on the static Stackelberg game between the Distribution Centers (DCs), Customer Zones (CZs) and Recover Centers (RCs) in the framework. The literature reports that most of previous works have utilized the various exact approaches which are not efficient and are so complex. In this study, three old and successful methods consist of Variable Neighborhood Search (VNS), Tabu Search (TS) and Particle Swarm Optimization (PSO), as well as two recent nature-inspired algorithms; Keshtel Algorithm (KA) and Water Wave Optimization (WWO) are utilized. Besides, according the nature of the problem, this study proposes a simple nested approach named as tri-level metaheuristic for the first time in order to solve the large scale problems. The performances of the algorithms are probed by using Taguchi experimental method to set the proper values for the parameters. Eventually, the efficiency of the algorithms is compared by different criteria and validated through a real case study. The obtained results show that tri-level metaheuristics are effective approaches to solve the underlying tri-level models in large scale network.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.11.004
      Issue No: Vol. 62 (2017)
       
  • An approximate/exact objective based search technique for solving general
           scheduling problems
    • Authors: Andrzej Kozik; Radosław Rudek
      Pages: 347 - 358
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Andrzej Kozik, Radosław Rudek
      In this paper, we analyze single machine scheduling problems under the following minimization objectives: the maximum completion time (makespan), the total completion time and the maximum lateness, including fundamental practical aspects, which often occur in industrial or manufacturing reality: release dates, due dates, setup times, precedence constraints, deterioration (aging) of machines, as well as maintenance activities. To solve the problems, we propose an efficient representation of a solution and a fast neighborhood search technique, which calculates an approximation of criterion values in a constant time per solution in a neighborhood. On this basis, a novel approximate/exact search technique, using exact as well as approximate criterion values during search process, is introduced and used to develop efficient metaheuristic algorithms dedicated to the considered problems. Their efficiency is verified during computational experiments.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.10.043
      Issue No: Vol. 62 (2017)
       
  • Intelligent computing for Mathieu’s systems for parameter excitation,
           vertically driven pendulum and dusty plasma models
    • Authors: Muhammad Asif Zahoor Raja; Muhammad Anwaar Manzar; Fiaz Hussain Shah; Fazal Hakim Shah
      Pages: 359 - 372
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Muhammad Asif Zahoor Raja, Muhammad Anwaar Manzar, Fiaz Hussain Shah, Fazal Hakim Shah
      In this study, a computational intelligence technique is developed to find the solutions of nonlinear Mathieu system arising in parameter excitation, vertically derive pendulum and dusty plasma studies using the strength of artificial neural networks in the modeling of equation and effective optimization of the error function through bioinspired heuristics based on global search with genetic algorithm and rapid local convergence with interior-point algorithm. The proposed scheme is applied to number of scenarios for Mathieu system to analyze the its dynamics for parameter excitation, oscillatory pendulum and dusty plasma models. The comparison of the proposed solutions with numerical results shows a close match which establishes its correctness. The consistent accuracy of the proposed solver is verified through results of statistics in terms of different performance indices based on mean absolute error, root mean square error and Nash-Sutcliffe efficiency. These solutions greatly enrich stochastic numerical solver for the celebrated Mathieu systems.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.10.049
      Issue No: Vol. 62 (2017)
       
  • Design of neuro-evolutionary model for solving nonlinear singularly
           perturbed boundary value problems
    • Authors: Muhammad Asif Zahoor Raja; Saleem Abbas; Muhammed Ibrahem Syam; Abdul Majid Wazwaz
      Pages: 373 - 394
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Muhammad Asif Zahoor Raja, Saleem Abbas, Muhammed Ibrahem Syam, Abdul Majid Wazwaz
      In this study, a neuro-evolutionary technique is developed for solving singularly perturbed boundary value problems (SP-BVPs) of linear and nonlinear ordinary differential equations (ODEs) by exploiting the strength of feed-forward artificial neural networks (ANNs), genetic algorithms (GAs) and sequential quadratic programming (SQP) technique. Mathematical modeling of SP-BVPs is constructed by using a universal function approximation capability of ANNs in mean square sense. Training of design parameter of ANNs is carried out by GAs, which is used as a tool for effective global search method integrated with SQP algorithm for rapid local convergence. The performance of the proposed design scheme is tested for six linear and nonlinear BVPs of singularly perturbed systems. Comprehensive numerical simulation studies are conducted to validate the effectiveness of the proposed scheme in terms of accuracy, robustness and convergence.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.11.002
      Issue No: Vol. 62 (2017)
       
  • Partitioned Heronian means based on linguistic intuitionistic fuzzy
           numbers for dealing with multi-attribute group decision making
    • Authors: Peide Liu; Junlin Liu; José M. Merigó
      Pages: 395 - 422
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Peide Liu, Junlin Liu, José M. Merigó
      Heronian mean (HM) operator has the advantages of considering the interrelationships between parameters, and linguistic intuitionistic fuzzy number (LIFN), in which the membership and non-membership are expressed by linguistic terms, can more easily describe the uncertain and the vague information existing in the real world. In this paper, we propose the partitioned Heronian mean (PHM) operator which assumes that all attributes are partitioned into several parts and members in the same part are interrelated while in different parts there are no interrelationships among members, and develop some new operational rules of LIFNs to consider the interactions between membership function and non-membership function, especially when the degree of non-membership is zero. Then we extend PHM operator to LIFNs based on new operational rules, and propose the linguistic intuitionistic fuzzy partitioned Heronian mean (LIFPHM) operator, the linguistic intuitionistic fuzzy weighted partitioned Heronian mean (LIFWPHM) operator, the linguistic intuitionistic fuzzy partitioned geometric Heronian mean (LIFPGHM) operator and linguistic intuitionistic fuzzy weighted partitioned geometric Heronian mean (LIFWPGHM) operator. Further, we develop two methods to solve multi-attribute group decision making (MAGDM) problems with the linguistic intuitionistic fuzzy information. Finally, we give some examples to verify the effectiveness of two proposed methods by comparing with the existing
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.10.017
      Issue No: Vol. 62 (2017)
       
  • T-operators in hesitant fuzzy sets and their applications to fuzzy
           rule-based classifier
    • Authors: M. Ranjbar; S. Effati; A.V. Kamyad
      Pages: 423 - 440
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): M. Ranjbar, S. Effati, A.V. Kamyad
      Extending of the T-operators on hesitant fuzzy sets (HFSs) is important in the theory and applications. In this paper, we used two comparative operators for expressing monotonicity of the T-operators on hesitant fuzzy elements (HFEs). Hence, properties of some T-operators for Xu-Xia partial order ( ≤ ℍ ( m ) ) and Xia-Xu order (⪯) are studied on typical hesitant fuzzy elements (THFEs). Finally, an application of T-operators on HFEs has been explained in fuzzy rule-based systems for a practical problem in economics. Also, for a conceptual comparison, a benchmark data under a hesitant fuzzy information environment has been used.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.10.016
      Issue No: Vol. 62 (2017)
       
  • Whale optimization approaches for wrapper feature selection
    • Authors: Majdi Mafarja; Seyedali Mirjalili
      Pages: 441 - 453
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Majdi Mafarja, Seyedali Mirjalili
      Classification accuracy highly dependents on the nature of the features in a dataset which may contain irrelevant or redundant data. The main aim of feature selection is to eliminate these types of features to enhance the classification accuracy. The wrapper feature selection model works on the feature set to reduce the number of features and improve the classification accuracy simultaneously. In this work, a new wrapper feature selection approach is proposed based on Whale Optimization Algorithm (WOA). WOA is a newly proposed algorithm that has not been systematically applied to feature selection problems yet. Two binary variants of the WOA algorithm are proposed to search the optimal feature subsets for classification purposes. In the first one, we aim to study the influence of using the Tournament and Roulette Wheel selection mechanisms instead of using a random operator in the searching process. In the second approach, crossover and mutation operators are used to enhance the exploitation of the WOA algorithm. The proposed methods are tested on standard benchmark datasets and then compared to three algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), the Ant Lion Optimizer (ALO), and five standard filter feature selection methods. The paper also considers an extensive study of the parameter setting for the proposed technique. The results show the efficiency of the proposed approaches in searching for the optimal feature subsets.
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      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.11.006
      Issue No: Vol. 62 (2017)
       
  • The bare bones fireworks algorithm: A minimalist global optimizer
    • Authors: Junzhi Li; Ying Tan
      Pages: 454 - 462
      Abstract: Publication date: January 2018
      Source:Applied Soft Computing, Volume 62
      Author(s): Junzhi Li, Ying Tan
      The fireworks algorithm is a newly proposed swarm algorithm for global optimization, which adopts a novel manner of search called explosion. In this paper, we introduce a simplified version of the fireworks algorithm, where only the essential explosion operation is kept, called the bare bones fireworks algorithm. The bare bones fireworks algorithm is simple, fast and easy to implement. Sufficient conditions for local convergence are given. Experimental results on benchmark functions and real-world problems indicate that its performance is competitive and serviceable and it is extremely efficient.

      PubDate: 2017-11-16T11:00:06Z
      DOI: 10.1016/j.asoc.2017.10.046
      Issue No: Vol. 62 (2017)
       
  • Hierarchical genetic-particle swarm optimization for bistable permanent
           magnet actuators
    • Authors: Cao Tan; Siqin Chang; Liang Liu
      Pages: 1 - 7
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Cao Tan, Siqin Chang, Liang Liu
      Bistable permanent magnet actuator (BPMA) has been widely used in industry as a kind of energy converter. However, its power consumption and response performance are in conflict during operating because of the conflict between holding force and initial force (the minimum threshold driving force). An optimization procedure based on a novel hierarchical genetic-particle swarm algorithm (HGP) for a hybrid excited linear actuator (HELA) constrained in a specific volume has been developed. For obtaining the objective function for each parameter combination precisely, three-dimensional finite element analysis (FEA) was employed. Meanwhile, the improvement of optimization efficiency and quality is extremely demanding because the FEA needs much computing time. The proposed hybrid algorithm adopts a hierarchical structure from the concept of “experimental classes” in China. The base level is composed of the majority general individuals of Genetic algorithms (GA), which provides the global search ability of the entire algorithm. The top level comprises the minority elite consisting of the better individual, which performs the accurate local search by using the particle swarm optimization (PSO) algorithm with variable inertia weight and constriction coefficient. Additionally, the performance of HGP has been evaluated through the Rosenbrock function, and the proposed method is superior to other related methods Finally, the proposed procedure was verified by optimizing HELA's parameters in multidimensional parameters space under the design constraint. The results show that both of the holding force and the initial force are improved more than 25% compared with the initial design, which ensures both low power consumption and fast response.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.044
      Issue No: Vol. 61 (2017)
       
  • Ensemble of texture descriptors for face recognition obtained by varying
           feature transforms and preprocessing approaches
    • Authors: Loris Nanni; Alessandra Lumini; Sheryl Brahnam
      Pages: 8 - 16
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Loris Nanni, Alessandra Lumini, Sheryl Brahnam
      This paper presents a novel ensemble of descriptors for face recognition derived from the base Patterns of the Oriented Edge Magnitudes (POEM) descriptor. Starting from different texture descriptors recently proposed in the literature, namely, the base patterns of POEM and the Monogenic Binary Coding (MBC), we develop different ensembles by varying the preprocessing techniques, the subspace projections, and some parameters of the system. Our approach is tested on the FERET datasets and the Labeled Faces in the Wild (LFW) dataset. Our system performs well on both datasets, obtaining, to the best of our knowledge, one of the highest performance rates published in the literature on the FERET datasets with an average accuracy of 97.3%. We want to stress that our ensemble obtains outstanding results in both datasets without any supervised approach or transform. The main findings of our proposed system include the following: 1) significant improvement in performance can be obtained by simply varying the parameters of stand-alone descriptors; and 2) performance can be improved by combining different enhancement and feature transform techniques.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.057
      Issue No: Vol. 61 (2017)
       
  • Analysis and validation of wavelet transform based DC fault detection in
           HVDC system
    • Authors: Yew Ming Yeap; Nagesh Geddada; Abhisek Ukil
      Pages: 17 - 29
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Yew Ming Yeap, Nagesh Geddada, Abhisek Ukil
      Fault detection plays an important role in both conventional AC and upcoming DC power systems. This paper aims to study the application of discrete wavelet transform (WT) for detecting the DC fault in the high voltage DC (HVDC) system. The methods of choosing the mother wavelet suited for DC fault is presented, based on degree of correlation to the fault pattern and the time delay. The wavelet analysis is performed on a multi-terminal HVDC system, built in PSCAD/EMTDC software. Its performance is judged for critical parameter like the fault location, resistance and distance. The analysis is further extended to validation using results from experiment, which is obtained from a lab-scale DC hardware setup. Load change, one of the transient disturbances in power system, is carried out to understand the effectiveness of the wavelet transform to differentiate it from the DC fault. The noise in the experimental result gives rise to non-zero wavelet coefficient during the steady-state. This can be improved by removing the unwanted noise using right filter while still retaining the fault-induced transient. The wavelet transform is compared with short-time Fourier transform to highlight the issue with window size and noise.

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.039
      Issue No: Vol. 61 (2017)
       
  • Learning and surface boundary feedbacks for colour natural scene
           perception
    • Authors: Francisco J. Díaz-Pernas; Mario Martínez-Zarzuela; Míriam Antón-Rodríguez; David González-Ortega
      Pages: 30 - 41
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Francisco J. Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez, David González-Ortega
      Boundary detection and segmentation are essential stages in object recognition and scene understanding. In this paper, we present a bio-inspired neural model of the ventral pathway for colour contour and surface perception, called LPREEN (Learning and Perceptual boundaRy rEcurrent dEtection Neural architecture). LPREEN models colour opponent processes and feedback interactions between cortical areas V1, V2, V4, and IT, which produce top-down and bottom-up information fusion. We suggest three feedback interactions that enhance and complete boundaries. Our proposed neural model contains a contour learning feedback that enhances the most probable contour positions in V1 according to a previous experience, and generates a surface perception in V4 through diffusion processes. We compared the proposed model with another bio-inspired model and two well-known contour extraction methods, using the Berkeley Segmentation Benchmark. LPREEN showed better performance than two methods and slightly worse performance than another one.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.055
      Issue No: Vol. 61 (2017)
       
  • On the effectiveness of feature selection methods for gait classification
           under different covariate factors
    • Authors: Tze Wei Yeoh; Fabio Daolio; Hernán E. Aguirre; Kiyoshi Tanaka
      Pages: 42 - 57
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Tze Wei Yeoh, Fabio Daolio, Hernán E. Aguirre, Kiyoshi Tanaka
      Gait classification is the problem of recognising individuals by the way in which they walk. The presence of covariate factors such as different clothing types, carrying conditions, walking surfaces, etc., can seriously complicate the task. Clothing, for instance, can occlude a significant amount of gait features and make human recognition difficult. Since the location of occlusion may differ for different covariate factors, relevant gait features may become irrelevant when the covariate factor changes, and exploiting occluded gait features can hinder the recognition performance. Therefore, feature selection has become an important step to make the analysis more manageable and to extract useful information for the gait classification task. Nevertheless, although feature selection is often used in order to identify the relevant body parts, to the best of our knowledge, a comparative analysis of feature selection techniques in gait recognition is seldom addressed. In this paper, we present an empirical approach to evaluate the degree of consistency among the performance of different selection algorithms in the context of gait identification under the effect of various covariate factors. First, a model-based framework for extracting informative gait features is introduced, then, an extensive comparative analysis of feature selection approaches in gait recognition is carried out. We perform a statistical study via ANOVA and mixed-effects models to examine the effect of six popular selection feature methods across classifiers and covariates. In addition, we systematically compare the selected feature subsets and the computational cost of the different selection approaches. The implemented method addresses the problem of feature selection for gait recognition on two well-known benchmark databases: the SOTON covariate database and the CASIA-B dataset, respectively. The investigated approach is able to select the discriminative input gait features and achieve an improved classification accuracy on par with other state-of-the-art methods.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.041
      Issue No: Vol. 61 (2017)
       
  • Scheduling of short-term hydrothermal energy system by parallel
           multi-objective differential evolution
    • Authors: Zhong-kai Feng; Wen-jing Niu; Jian-zhong Zhou; Chun-tian Cheng; Yong-chuan Zhang
      Pages: 58 - 71
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Zhong-kai Feng, Wen-jing Niu, Jian-zhong Zhou, Chun-tian Cheng, Yong-chuan Zhang
      With the growing concerns on energy and environment, the short-term hydrothermal scheduling (SHTS) which minimizes the fuel cost and pollutant emission simultaneously is playing an increasing important role in the modern electric power system. Due to the complicated operation constraints and objectives, SHTS is classified as a multi-objective optimization problem. Thus, to efficiently resolve this problem, this paper develops a novel parallel multi-objective differential evolution (PMODE) combining the merits of parallel technology and multi-objective differential evolution. In PMODE, the population with larger size is first divided into several smaller subtasks to be concurrently executed in different computing units, and then the main thread collects the results of each subpopulation to form the final Pareto solutions set for the SHTS problem. During the evolutionary process of each subpopulation, the mutation crossover and selection operators are modified to enhance the performance of population. Besides, an external archive set is used to conserve the Pareto solutions and provide multiple evolutionary directions for individuals, while the constraint handling method is introduced to address the complicated operational constraints. The results from a mature hydrothermal system indicate that when compared with several existing methods, PMODE can obtain satisfactory solutions in both fuel cost and environmental pollutant, which is an effective tool to generate trade-off schemes for the hydrothermal scheduling problem.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.054
      Issue No: Vol. 61 (2017)
       
  • Semi-supervised matrixized least squares support vector machine
    • Authors: Huimin Pei; Kuaini Wang; Ping Zhong
      Pages: 72 - 87
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Huimin Pei, Kuaini Wang, Ping Zhong
      The matrix learning, which studies how to design algorithms based on matrix patterns, is proven to have some significant advantages over the vector learning such as the improved classification performance and the low computational complexity. However, most of the traditional matrix learning algorithms are supervised ones which require labels of all patterns. In practice, the difficult acquisition of labeled patterns is a major challenge for supervised algorithms. An effective approach to handle this problem is the manifold regularization, which is known as one of the most elegant frameworks for the semi-supervised learning (SSL). The Laplacian regularized least squares (LapRLS) is a classical vector learning algorithm following this framework. Inspired by the advantages of the matrix learning and the SSL, in this paper, we propose a novel semi-supervised matrix learning algorithm by incorporating the manifold regularization into the matrixized least squares support vector machine (MatLSSVM), termed as Laplacian matrixized LSSVM, or LapMatLSSVM for short. MatLSSVM, which has been built by combining the merits of the matrix learning and LSSVM, is a promising supervised algorithm. As an extension of MatLSSVM to the SSL, LapMatLSSVM can not only directly operate on matrix patterns, but also effectively exploit the geometric information embedded in unlabeled matrix patterns. Moreover, its generalization risk bound is tighter than that of LapRLS in terms of the Rademacher complexity. For the implementation, LapMatLSSVM learns in an iterative manner, and solves a least squares optimization problem at each iteration. Extensive experiments have been conducted across two kinds of datasets: image datasets and UCI datasets. Experimental results confirm the benefits of the proposed algorithm.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.040
      Issue No: Vol. 61 (2017)
       
  • A new approach for multiple attribute group decision making with
           interval-valued intuitionistic fuzzy information
    • Authors: Junda Qiu; Lei Li
      Pages: 111 - 121
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Junda Qiu, Lei Li
      This paper proposes a novel method for multiple attribute group decision making (MAGDM) with interval-valued intuitionistic fuzzy information. The interval-valued intuitionistic fuzzy numbers of each expert preference matrix are first mapped into two dimensions. Thus, the values of each membership degree and non-membership degree are considered as points in the two-dimensional representation. Moreover, the distance between the points represents the variance among the different experts preferences. The preference points of the same character are considered as a point set. We employ the plant growth simulation algorithm (PGSA) to calculate the optimal rally points of every point set, the sum of whose Euclidean distances to other given points is minimal, and these optimal rally points reflect the preferences of the entire expert group. These points are used to establish an expert preference aggregation matrix. Suitable points from the matrix are chosen to constitute an ideal point matrix, a projection method is employed to calculate the sum of its Euclidean distance to the expert preference aggregation matrix, and the score of each alternative is evaluated. Finally, the overall ranking of alternatives is obtained. In addition, this study develops a process to evaluate the pros and cons of different aggregation methods. Two typical examples are presented to illustrate the feasibility and effectiveness of the proposed approach.

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.008
      Issue No: Vol. 61 (2017)
       
  • Hybrid flow shop scheduling with assembly operations and key objectives: A
           novel neighborhood search
    • Authors: Deming Lei; Youlian Zheng
      Pages: 122 - 128
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Deming Lei, Youlian Zheng
      This paper addresses hybrid flow shop scheduling problem (HFSP) with assembly operations, in which parts of each product are produced in a hybrid flow shop and then assembled at an assembly stage. The goal is to minimize total tardiness, maximum tardiness and makespan simultaneously. Tardiness objectives are regarded as key ones because of their relative importance and this situation is seldom considered. A simple strategy is applied to handle the optimization with key objectives. A novel neighborhood search with global exchange (NSG) is proposed, in which a part-based coding method is adopted and global exchange is cooperated with neighborhood search to produce high quality solution. Extensive experiments are conducted and the results show that the strategy on key objectives is reasonable and effective and NSG is a very competitive method for the considered HFSP.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.058
      Issue No: Vol. 61 (2017)
       
  • A beam search approach for solving type II robotic parallel assembly line
           balancing problem
    • Authors: Zeynel Abidin Çil; Süleyman Mete; Eren Özceylan; Kürşad Ağpak
      Pages: 129 - 138
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Zeynel Abidin Çil, Süleyman Mete, Eren Özceylan, Kürşad Ağpak
      In a robotic assembly line, a series of stations are arranged along a conveyor belt and a robot performs on tasks at each station. Parallel assembly lines can provide improving line balance, productivity and so on. Combining robotic and parallel assembly lines ensure increasing flexibility of system, capacity and decreasing breakdown sensitivity. Although aforementioned benefits, balancing of robotic parallel assembly lines is lacking – to the best knowledge of the authors- in the literature. Therefore, a mathematical model is proposed to define/solve the problem and also iterative beam search (IBS), best search method based on IBS (BIBS) and cutting BIBS (CBIBS) algorithms are presented to solve the large-size problem due to the complexity of the problem. The algorithm also tested on the generated benchmark problems for robotic parallel assembly line balancing problem. The superior performances of the proposed algorithms are verified by using a statistical test. The results show that the algorithms are very competitive and promising tool for further researches in the literature.
      Graphical abstract image

      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.062
      Issue No: Vol. 61 (2017)
       
  • A hyperparameters selection technique for support vector regression models
    • Authors: P. Tsirikoglou; S. Abraham; F. Contino; C. Lacor; G. Ghorbaniasl
      Pages: 139 - 148
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): P. Tsirikoglou, S. Abraham, F. Contino, C. Lacor, G. Ghorbaniasl
      Support vector regression models are powerful surrogates used in various fields of engineering. Due to the quality of their predictions and their efficiency, those models are considered as a suitable tool for surrogate evaluation. Despite their advantages, support vector regression models require an accurate selection of the configuration parameters in order to achieve good generalization performance. To overcome this limitation, a new hyperparameter selection method is developed. This method takes into account the training error to identify the optimal parameters set using evolutionary optimization schemes. Moreover, building on state-of-the-art techniques, an alternative analytically-assisted genetic algorithm is proposed in order to enhance the accuracy and robustness of the optimization scheme. The configuration is elaborated from a new search strategy in the design space. The results verify that the proposed technique improve the prediction accuracy and its robustness. Several test cases are used to demonstrate the capabilities of the method and its application potential to real engineering problems. The results prove that a surrogate model coupled with this adaptive configuration technique provides a useful prediction model suitable for various types of numerical experiments.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.017
      Issue No: Vol. 61 (2017)
       
  • Bio-inspired computation: Recent development on the modifications of the
           cuckoo search algorithm
    • Authors: Haruna Chiroma; Tutut Herawan; Iztok Fister; Iztok Fister; Sameem Abdulkareem; Liyana Shuib; Mukhtar Fatihu Hamza; Younes Saadi; Adamu Abubakar
      Pages: 149 - 173
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Haruna Chiroma, Tutut Herawan, Iztok Fister, Iztok Fister, Sameem Abdulkareem, Liyana Shuib, Mukhtar Fatihu Hamza, Younes Saadi, Adamu Abubakar
      Presently, the Cuckoo Search algorithm is attracting unprecedented attention from the research community and applications of the algorithm are expected to increase in number rapidly in the future. The purpose of this study is to assist potential developers in selecting the most suitable cuckoo search variant, provide proper guidance in future modifications and ease the selection of the optimal cuckoo search parameters. Several researchers have attempted to apply several modifications to the original cuckoo search algorithm in order to advance its effectiveness. This paper reviews the recent advances of these modifications made to the original cuckoo search by analyzing recent published papers tackling this subject. Additionally, the influences of various parameter settings regarding cuckoo search are taken into account in order to provide their optimal settings for specific problem classes. In order to estimate the qualities of the modifications, the percentage improvements made by the modified cuckoo search over the original cuckoo search for some selected reviews studies are computed. It is found that the population reduction and usage of biased random walk are the most frequently used modifications. This study can be used by both expert and novice researchers for outlining directions for future development, and to find the best modifications, together with the corresponding optimal setting of parameters for specific problems. The review can also serve as a benchmark for further modifications of the original cuckoo search.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.053
      Issue No: Vol. 61 (2017)
       
  • A bi-objective batch processing problem with dual-resources on
           unrelated-parallel machines
    • Authors: Omid Shahvari; Rasaratnam Logendran
      Pages: 174 - 192
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Omid Shahvari, Rasaratnam Logendran
      In this paper, a bi-objective batch processing problem with dual-resources on unrelated-parallel machines is addressed. Typically, both machines and operators as dual-resources are required to process the setup and run of any batch on any machine. The pursuit of this research is motivated by the adoption of the just-in-time philosophy on unrelated-parallel machines, where both the production time and cost are considered. A mathematical programming model is proposed in four layers for simultaneously minimizing the production cost including total cost of tardy and early jobs along with total batch processing cost as well as the makespan with dual-resources. Four bi-objective particle swarm optimization based search algorithms are proposed to efficiently solve the optimization problem for medium- and large-size instances. To reflect the real industry requirements, dynamic machine and operator availability times, dynamic job release times, machine eligibility and capability for processing jobs, batch capacity limitations, machine-dependent setup time, and different skill levels of operators are considered. Several numerical examples are generated by a comprehensive data generation mechanism to compare the search algorithms with respect to four performance indicators. A comparison of small-size instances between performance indicators of the best search algorithm and optimization method shows the same performance to find non-dominated solutions in the final Pareto optimal solutions, while the best search algorithm spreads and extends more in the entire Pareto optimal region and shows that it is less capable in converging to the true Pareto optimal front. To the best of our knowledge, this research is the first of its kind to provide a comprehensive mathematical model for bi-objective batch processing problem with dual-resources.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.08.014
      Issue No: Vol. 61 (2017)
       
  • Downsizing training data with weighted FCM for predicting the evolution of
           specific grinding energy with RNNs
    • Authors: A. Arriandiaga; E. Portillo; J.A. Sánchez; I. Cabanes; Asier Zubizarreta
      Pages: 211 - 221
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): A. Arriandiaga, E. Portillo, J.A. Sánchez, I. Cabanes, Asier Zubizarreta
      Grinding plays a prominent role in modern manufacturing due to its capacity for producing parts of high accuracy and precision. Among the various grinding variables, the specific grinding energy (ec) is key because it measures the energy required to remove the unit volume of part material and therefore it gives information about the performance of the grinding process. In addition, a measure of the specific grinding energy is also useful for estimating the power requirements of the grinding machine. Thus, a Recurrent Neural Network (RNN) is used for predicting ec in a more realistic manner that involves the development of the ec over time. Moreover, since performing grinding experiments is a highly time and resource consuming task, it would be very useful to downsize the required dataset to train the RNN since it could substantially reduce the time and costs involved in carrying out the experiments to generate the dataset, as well as in training the RNNs. Therefore, in this work a methodology combining Fuzzy C-Means (FCM) and RNNs for downsizing the dataset and predicting specific grinding energy is proposed. Unlike other approaches for reducing the dataset using FCM, in the current work the inputs are weighted. To achieve this, the knowledge is extracted from the weights of satisfactorily trained RNN obtained from previous work. The results show that under reduced training datasets (weighted and non-weighted FCM inputs) and non-reduced datasets (all available experiments), superior results were yielded with the RNNs obtained with the weighted approach. In fact, in some cases, for the reduced training dataset (weighted) the error is halved. Furthermore, the results show that it is more advantageous to use a reduced training dataset obtained after FCM, since this reduces the costs associated with experimental time, as well as the training time required for RNNs.

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.07.048
      Issue No: Vol. 61 (2017)
       
  • A bi-level school bus routing problem with bus stops selection and
           possibility of demand outsourcing
    • Authors: Seyed Parsa Parvasi; Mehdi Mahmoodjanloo; Mostafa Setak
      Pages: 222 - 238
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Seyed Parsa Parvasi, Mehdi Mahmoodjanloo, Mostafa Setak
      This paper develops a bi-level mathematical model for the school bus routing problem aiming at designing an efficient transportation system considering the possibility of predicting the students’ response. In the real world, the demand for using private cars depends on how well public transportation systems are operating especially in metropolitan cities. An inefficient public transportation will lead to an increase in the demand for using private cars. This issue will result in problems such as increased traffics and urban pollutions. To address this issue, an efficient public transportation system is designed by developing a new bi-level mathematical model. In the proposed model, the designer of the public transportation system, as the upper-level decision-maker, will locate appropriate bus stops and identify bus navigation routes. Subsequently, the decision regarding the allocation of students to transportation systems or outsourcing them will be made at the lower level which is considered as an operational-level decision-making. To solve this problem, two hybrid metaheuristic approaches named GA-EX-TS and SA-EX-TS have been proposed based on location-allocation-routing (LAR) strategy. The performance of these proposed methods is compared with exact solutions achieved from an explicit enumeration approach followed in the small-scale instances. Finally, the proposed approaches are used to solve 50 random instance problems. Comparing the results of the two tuned hybrid algorithms and conducting the sensitivity analysis of the model provide evidence for the good performance of the proposed approach.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
      DOI: 10.1016/j.asoc.2017.08.018
      Issue No: Vol. 61 (2017)
       
  • A Physarum-inspired optimization algorithm for load-shedding problem
    • Authors: Chao Gao; Shi Chen; Xianghua Li; Jiajin Huang; Zili Zhang
      Pages: 239 - 255
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Chao Gao, Shi Chen, Xianghua Li, Jiajin Huang, Zili Zhang
      Load-shedding is an intentional reduction approach which can maintain the stability of a microgrid system effectively. Recent studies have shown that a load-shedding problem can be solved by formulating it as a 0/1 knapsack problem (KP). Although approximate solutions of 0/1 KP can be given by ant colony optimization (ACO) algorithms, adopting them requests a delicate consideration of the robustness, convergence rate and premature convergence. This paper proposes a new kind of Physarum-based hybrid optimization algorithm, denoted as PM-ACO, based on the critical paths reserved feature of Physarum-inspired mathematical (PM) model. Through adding additional pheromone to those important items selected by the PM model, PM-ACO improves the selection probability of important items and emerge a positive feedback process to generate optimal solutions. Comparing with other 0/1 KP solving algorithms, our experimental results demonstrate that PM-ACO algorithms have a stronger robustness and a higher convergence rate. Moreover, PM-ACO provides adaptable solutions for the load-shedding problem in a microgrid system.
      Graphical abstract image

      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.043
      Issue No: Vol. 61 (2017)
       
  • An extended teaching-learning based optimization algorithm for solving
           no-wait flow shop scheduling problem
    • Authors: Weishi Shao; Dechang Zhongshi Shao
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Weishi Shao, Dechang Pi, Zhongshi Shao
      The no-wait flow shop scheduling problem (NWFSSP) performs an important function in the manufacturing industry. Inspired by the overall process of teaching-learning, an extended framework of meta-heuristic based on the teaching-learning process is proposed, which consists of four parts, i.e. previewing before class, teaching phase, learning phase, reviewing after class. This paper implements a hybrid meta-heuristic based on probabilistic teaching-learning mechanism (mPTLM) to solve the NWFSSP with the makespan criterion. In previewing before class, an initial method that combines a modified Nawaz-Enscore-Ham (NEH) heuristic and the opposition-based learning (OBL) is introduced. In teaching phase, the Gaussian distribution is employed as the teacher to guide learners to search more promising areas. In learning phase, this paper presents a new means of communication with crossover. In reviewing after class, an improved speed-up random insert local search based on simulated annealing (SA) is developed to enhance the local searching ability. The computational results and comparisons based on Reeves, Taillard and VRF’s benchmarks demonstrate the effectiveness of mPTLM for solving the NWFSSP.
      Graphical abstract image

      PubDate: 2017-08-31T02:52:27Z
       
 
 
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