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COMPUTER SCIENCE (1160 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: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 73)
ACM Computing Surveys     Hybrid Journal   (Followers: 22)
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: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 13)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 4)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 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: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 11)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 22)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 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: 13)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
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: 37)
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: 7)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 8)
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: 9)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 14)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 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: 128)
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  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 9)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 301)
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: 126)
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: 11)
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: 85)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 7)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 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: 11)
Computer Science Education     Hybrid Journal   (Followers: 12)
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  [3043 journals]
  • 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
    • 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.
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      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.
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      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.
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      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.
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      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.
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      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.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.043
      Issue No: Vol. 61 (2017)
  • Multiobjective coverage path planning: Enabling automated inspection of
           complex, real-world structures
    • Authors: K.O. Ellefsen; H.A. Lepikson; J.C. Albiez
      Pages: 264 - 282
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): K.O. Ellefsen, H.A. Lepikson, J.C. Albiez
      An important open problem in robotic planning is the autonomous generation of 3D inspection paths – that is, planning the best path to move a robot along in order to inspect a target structure. We recently suggested a new method for planning paths allowing the inspection of complex 3D structures, given a triangular mesh model of the structure. The method differs from previous approaches in its emphasis on generating and considering also plans that result in imperfect coverage of the inspection target. In many practical tasks, one would accept imperfections in coverage if this results in a substantially more energy efficient inspection path. The key idea is using a multiobjective evolutionary algorithm to optimize the energy usage and coverage of inspection plans simultaneously – and the result is a set of plans exploring the different ways to balance the two objectives. We here test our method on a set of inspection targets with large variation in size and complexity, and compare its performance with two state-of-the-art methods for complete coverage path planning. The results strengthen our confidence in the ability of our method to generate good inspection plans for different types of targets. The method's advantage is most clearly seen for real-world inspection targets, since traditional complete coverage methods have no good way of generating plans for structures with hidden parts. Multiobjective evolution, by optimizing energy usage and coverage together, ensures a good balance between the two – both when 100% coverage is feasible, and when large parts of the object are hidden.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.051
      Issue No: Vol. 61 (2017)
  • Using variable reduction strategy to accelerate evolutionary optimization
    • Authors: Guohua Wu; Witold Pedrycz; P.N. Suganthan; Haifeng Li
      Pages: 283 - 293
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Guohua Wu, Witold Pedrycz, P.N. Suganthan, Haifeng Li
      In this study, we introduce a novel approach of variable reduction and integrate it into evolutionary algorithms in order to reduce the complexity of optimization problems. We develop reduction processes of variable reduction for derivative unconstrained optimization problems (DUOPs) and constrained optimization problems (COPs) with equality constraints and active inequality constraints. Variable reduction uses the problem domain knowledge implied when investigating optimal conditions existing in optimization problems. For DUOPs, equations involving derivatives are considered while for COPs, we discuss equations expressing the equality constraints. From the relationships formed in this way, we obtain relationships among the variables that have to be satisfied by optimal solutions. According to such relationships, we can utilize some variables (referred to as core variables) to express some other variables (referred to as reduced variables). We show that the essence of variable reduction is to produce a minimum collection of core variables and a maximum number of reduced variables based on a system of equations. We summarize some application-oriented situations of variable reduction and stress several important issues related to the further application and development of variable reduction. Essentially, variable reduction can reduce the number of variables and eliminate equality constraints, thus reducing the dimensionality of the solution space and improving the efficiency of evolutionary algorithms. The approach can be applied to unconstrained, constrained, continuous and discrete optimization problems only if there are explicit variable relationships to be satisfied in the optimal conditions. We test variable reduction on real-world and synthesized DUOPs and COPs. Experimental results and comparative studies point at the effectiveness of variable reduction.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.012
      Issue No: Vol. 61 (2017)
  • A multiobjective box-covering algorithm for fractal modularity on complex
    • Authors: Hongrun Wu; Li Kuang; Feng Wang; Qi Rao; Maoguo Gong; Yuanxiang Li
      Pages: 294 - 313
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Hongrun Wu, Li Kuang, Feng Wang, Qi Rao, Maoguo Gong, Yuanxiang Li
      The box-covering method is widely used on measuring the fractal property on complex networks. The problem of finding the minimum number of boxes to tile a network is known as a NP-hard problem. Many algorithms have been proposed to solve this problem. All the current box-covering algorithms regard the box number minimization as the only objective. However, the fractal modularity of the network partition divided by the box-covering method, has been proved to be strongly related to the information transportation in complex networks. Maximizing the fractal modularity is also important in the box-covering method, which can be divided into two objectives: maximization of ratio association and minimization of ratio cut. In this paper, to solve the dilemma of minimizing the box number and maximizing the fractal modularity at the same time, a multiobjective discrete particle swarm optimization box-covering (MOPSOBC) algorithm is proposed. The MOPSOBC algorithm applies the decomposition approach on the two objectives to approximate the Pareto front. The proposed MOPSOBC algorithm has been applied to six benchmark networks and compared with the state-of-the-art algorithms, including two classical box-covering algorithms, four single objective optimization algorithms and six multiobjective optimization algorithms. The experimental results show that the MOPSOBC algorithm can get similar box numbers with the current best algorithm, and it outperforms the state-of-the-art algorithms on the fractal modularity and normalized mutual information.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.034
      Issue No: Vol. 61 (2017)
  • Particle swarm optimizer with two differential mutation
    • Authors: Yonggang Chen; Lixiang Li; Haipeng Peng; Jinghua Xiao; Yixian Yang; Yuhui Shi
      Pages: 314 - 330
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Yonggang Chen, Lixiang Li, Haipeng Peng, Jinghua Xiao, Yixian Yang, Yuhui Shi
      In this article, a particle swarm optimization algorithm with two differential mutation (PSOTD) is proposed. In PSOTD, a novel structure with two swarms and two layers (bottom layer and top layer) is designed. The top layer consists of all the personal best particles, and the bottom layer consists of all the particles. We divide the particles in the top layer into two sub-swarms. Two different differential mutation operations with two different control parameters are employed in order to breed the particles in the top layer. Thus, one sub-swarm has a good exploration capability, and the other sub-swarm has a good exploitation capability. Obviously, since the top layer leads the bottom layer, the bottom particles achieve a good trade-off between exploration and exploitation. Under the searching structure, PSO enhances the global search capability and search efficiency. In order to test the performance of PSOTD, 44 benchmark functions widely adopted in the literature are used. The experimental results demonstrate that the proposed PSOTD outperforms most of the other tested variants of the PSO in terms of both solution quality and efficiency.

      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.020
      Issue No: Vol. 61 (2017)
  • Parallel quantum-inspired evolutionary algorithms for community detection
           in social networks
    • Authors: Shikha Gupta; Stuti Mittal; Tamanna Gupta; Isha Singhal; Barkha Khatri; Ajay K. Gupta; Naveen Kumar
      Pages: 331 - 353
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Shikha Gupta, Stuti Mittal, Tamanna Gupta, Isha Singhal, Barkha Khatri, Ajay K. Gupta, Naveen Kumar
      The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers. In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization. In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs. The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.035
      Issue No: Vol. 61 (2017)
  • Convergence analysis of BP neural networks via sparse response
    • Authors: Jian Wang; Yanqing Wen; Zhenyun Ye; Ling Jian; Hua Chen
      Pages: 354 - 363
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Jian Wang, Yanqing Wen, Zhenyun Ye, Ling Jian, Hua Chen
      Backpropagation (BP) algorithm is the typical strategy to train the feedforward neural networks (FNNs). Gradient descent approach is the popular numerical optimal method which is employed to implement the BP algorithm. However, this technique frequently leads to poor generalization and slow convergence. Inspired by the sparse response character of human neuron system, several sparse-response BP algorithms were developed which effectively improve the generalization performance. The essential idea is to impose the responses of hidden layer as a specific L 1 penalty term on the standard error function of FNNs. In this paper, we mainly focus on the two remaining challenging tasks: one is to solve the non-differential problem of the L 1 penalty term by introducing smooth approximation functions. The other aspect is to provide a rigorous convergence analysis for this novel sparse response BP algorithm. In addition, an illustrative numerical simulation has been done to support the theoretical statement.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.059
      Issue No: Vol. 61 (2017)
  • Fuzzy clustering with nonlinearly transformed data
    • Authors: Xiubin Zhu; Witold Pedrycz; Zhiwu Li
      Pages: 364 - 376
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Xiubin Zhu, Witold Pedrycz, Zhiwu Li
      The Fuzzy C-Means (FCM) algorithm is a widely used objective function-based clustering method exploited in numerous applications. In order to improve the quality of clustering algorithms, this study develops a novel approach, in which a transformed data-based FCM is developed. Two data transformation methods are proposed, using which the original data are projected in a nonlinear fashion onto a new space of the same dimensionality as the original one. Next, clustering is carried out on the transformed data. Two optimization criteria, namely a classification error and a reconstruction error, are introduced and utilized to guide the optimization of the performance of the new clustering algorithm and a transformation of the original data space. Unlike other data transformation methods that require some prior knowledge, in this study, Particle Swarm Optimization (PSO) is used to determine the optimal transformation realized on a basis of a certain performance index. Experimental studies completed for a synthetic data set and a number of data sets coming from the Machine Learning Repository demonstrate the performance of the FCM with transformed data. The experiments show that the proposed fuzzy clustering method achieves better performance (in terms of the clustering accuracy and the reconstruction error) in comparison with the outcomes produced by the generic version of the FCM algorithm.

      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.026
      Issue No: Vol. 61 (2017)
  • Self-adjusting parameter control for surrogate-assisted constrained
           optimization under limited budgets
    • Authors: Samineh Bagheri; Wolfgang Konen; Michael Emmerich; Thomas Bäck
      Pages: 377 - 393
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Samineh Bagheri, Wolfgang Konen, Michael Emmerich, Thomas Bäck
      Constrained optimization of high-dimensional numerical problems plays an important role in many scientific and industrial applications. Function evaluations in many industrial applications are severely limited and often only little analytical information about objective function and constraint functions is available. For such expensive black-box optimization tasks, the constraint optimization algorithm COBRA (Constrained Optimization By Radial Basis Function Approximation) was proposed, making use of RBF (radial basis function) surrogate modeling for both objective and constraint functions. COBRA has shown remarkable success in solving reliably complex benchmark problems in less than 500 function evaluations. Unfortunately, COBRA requires careful adjustment of parameters in order to do so. In this work we present a new algorithm SACOBRA (Self-Adjusting COBRA), which is based on COBRA and capable of achieving high-quality results with very few function evaluations and no parameter tuning. It is shown with the help of performance profiles on a set of benchmark problems (G-problems, MOPTA08) that SACOBRA consistently outperforms COBRA algorithms with different fixed parameter settings. We analyze the importance of the new elements in SACOBRA and show that each element of SACOBRA plays a role to boost up the overall optimization performance. We discuss the reasons and get in this way a better understanding of high-quality RBF surrogate modeling.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.060
      Issue No: Vol. 61 (2017)
  • A many-objective evolutionary algorithm based on a projection-assisted
           intra-family election
    • Authors: Zefeng Chen; Yuren Zhou; Yi Xiang
      Pages: 394 - 411
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Zefeng Chen, Yuren Zhou, Yi Xiang
      In recent years, many researchers have put emphasis on the study of how to keep a good balance between convergence and diversity in many-objective optimization. This paper proposes a new many-objective evolutionary algorithm based on a projection-assisted intra-family election. In the proposed algorithm, basic evolution directions are adaptively generated according to the current population and potential evolution directions are excavated in each individual's family. Based on these evolution directions, a strategy of intra-family election is performed in every family and elite individuals are elected as representatives of the specific family to join the next stage, which can enhance the convergence of the algorithm. Moreover, a selection procedure based on angles is used to maintain the diversity. The performance of the proposed algorithm is verified and compared with several state-of-the-art many-objective evolutionary algorithms on a variety of well-known benchmark problems ranging from 5 to 20 objectives. Empirical results demonstrate that the proposed algorithm outperforms other peer algorithms in terms of both the diversity and the convergence of the final solutions set on most of the test instances. In particular, our proposed algorithm shows obvious superiority when handling the problems with larger number of objectives.

      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.052
      Issue No: Vol. 61 (2017)
  • Accurate segmentation of complex document image using digital shearlet
           transform with neutrosophic set as uncertainty handling tool
    • Authors: Soumyadip Dhar; Malay K. Kundu
      Pages: 412 - 426
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Soumyadip Dhar, Malay K. Kundu
      In any image segmentation problem, there exist uncertainties. These uncertainties occur from gray level and spatial ambiguities in an image. As a result, accurate segmentation of text regions from non-text regions (graphics/images) in mixed and complex documents is a fairly difficult problem. In this paper, we propose a novel text region segmentation method based on digital shearlet transform (DST). The method is capable of handling the uncertainties arising in the segmentation process. To capture the anisotropic features of the text regions, the proposed method uses the DST coefficients as input features to a segmentation process block. This block is designed using the neutrosophic set (NS) for management of the uncertainty in the process. The proposed method is experimentally verified extensively and the performance is compared with that of some state-of-the-art techniques both quantitatively and qualitatively using benchmark dataset.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.005
      Issue No: Vol. 61 (2017)
  • Efficient cardiac segmentation using random walk with pre-computation and
           intensity prior model
    • Authors: Osama S. Faragallah; Ghada Abdel-Aziz; Hamdy M. Kelash
      Pages: 427 - 446
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Osama S. Faragallah, Ghada Abdel-Aziz, Hamdy M. Kelash
      Cardiovascular diseases (CVDs) are considered the first cause of mortality and the major health concern according to recent statistics worldwide. Most CVDs are preventable by early prediction and detection to avoid the risk factors. Accurate early detection can strongly lower the death rate caused by CVDs. Image processing in field of biomedical analysis plays a major role in the detection of CVDs through the cardiac segmentation. The accurate and fast segmentation of left ventricle cavity and myocardium have a major effect on the quantification and diagnosis of the cardiac function. This research paper tackles the cardiac segmentation problem and presents a hybrid random walk segmentation technique for helping cardiologists and physicians to detect CVDs early. The proposed segmentation framework utilizes the toboggan segmentation algorithm, mixes the characteristics of the high speed random walk with pre-computation model and extended random walk with prior model to improve the segmentation. To assess the achievement of the suggested cardiac segmentation technique, a course of experiments is conducted using a 3D multi-slice short axis CMR database. The performance of the proposed technique is assessed and compared with that of other medical image segmentation techniques using various performance metrics such as similarity Dice coefficient, PSNR and Hausdorff distance. Compared to the other studied techniques, the results demonstrated that, the proposed technique is a powerful and accurate methodology in delineating the Left Ventricle (LV) endocardium and epicardium for segmenting the myocardium and cavity of LV. The LV parameters are then estimated using the obtained segments. The results also demonstrated that, the proposed technique improves the segmentation time significantly.

      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.002
      Issue No: Vol. 61 (2017)
  • Local search enhanced multi-objective PSO algorithm for scheduling textile
           production processes with environmental considerations
    • Authors: Rui Zhang; Pei-Chann Chang; Shiji Song; Cheng Wu
      Pages: 447 - 467
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Rui Zhang, Pei-Chann Chang, Shiji Song, Cheng Wu
      Textile dyeing often constitutes a bottleneck procedure in the production of clothing because the dyeing process is time-consuming and heavily constrained. Meanwhile, dyeing processes inevitably produce emissions of water pollutants especially when the involved equipment undergoes cleaning operations. Scheduling could be utilized as a system-level tool to reduce the amount of pollutant emission besides its normal role for improving the production performance (e.g., reducing delivery tardiness). To this end, we have formulated the textile dyeing process scheduling problem as a bi-objective optimization model, in which one objective is connected with tardiness cost while the other objective reflects the level of pollutant emission. Due to the NP -hard nature of the resulting problem, we have proposed a multi-objective particle swarm optimization algorithm enhanced by problem-specific local search techniques (MO-PSO-L) to seek high-quality non-dominated solutions. The proposed hybrid algorithm is characterized by a tailored solution-initialization method, a set of time-variant parameters and several unique mechanisms for dealing with multi-objective optimization (including density-oriented solution sorting and personal/global best solution handling). The local search technique based on ejection chains has been specifically designed for improving a number of promising solutions with a focus on the pollution-related objective. Superiority of the proposed solution approach has been verified by computational experiments on a large set of test instances together with fair comparisons with two state-of-the-art algorithms.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.013
      Issue No: Vol. 61 (2017)
  • Deterministic metaheuristic based on fractal decomposition for large-scale
    • Authors: A. Nakib; S. Ouchraa; N. Shvai; L. Souquet; E.-G. Talbi
      Pages: 468 - 485
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): A. Nakib, S. Ouchraa, N. Shvai, L. Souquet, E.-G. Talbi
      In this work a new method based on geometric fractal decomposition to solve large-scale continuous optimization problems is proposed. It consists of dividing the feasible search space into sub-regions with the same geometrical pattern. At each iteration, the most promising ones are selected and further decomposed. This approach tends to provide a dense set of samples and has interesting theoretical convergence properties. Under some assumptions, this approach covers all the search space only in case of small dimensionality problems. The aim of this work is to propose a new algorithm based on this approach with low complexity and which performs well in case of large-scale problems. To do so, a low complex method that profits from fractals properties is proposed. Then, a deterministic optimization procedure is proposed using a single solution-based metaheuristic which is exposed to illustrate the performance of this strategy. Obtained results on common test functions were compared to those of algorithms from the literature and proved the efficiency of the proposed algorithm.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.07.042
      Issue No: Vol. 61 (2017)
  • Minimizing harmonic distortion in power system with optimal design of
           hybrid active power filter using differential evolution
    • Authors: Partha P. Biswas; P.N. Suganthan; Gehan A.J. Amaratunga
      Pages: 486 - 496
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Partha P. Biswas, P.N. Suganthan, Gehan A.J. Amaratunga
      Hybrid active power filter (HAPF) is an advanced form of harmonic filter combining advantages of both active and passive filters. In HAPF, selection of active filter gain, passive inductive and capacitive reactances, while satisfying system constraints on individual and overall voltage and current harmonic distortion levels, is the main challenge. To optimize HAPF parameters, this paper proposes an approach based on differential evolution (DE) algorithm called L-SHADE. SHADE is the success history based parameter adaptation technique of DE optimization process for a constrained, multimodal non-linear objective function. L-SHADE improves the performance of SHADE with linearly reducing the population size in successive generations. The study herein considers two frequently used topologies of HAPF for parameter estimation. A single objective function consisting of both total voltage harmonic distortion (VTHD) and total current harmonic distortion (ITHD) is formulated and finally harmonic pollution (HP) is minimized in a system comprising of both non-linear source and non-linear loads. Several case studies of a selected industrial plant are performed. The output results of L-SHADE algorithm are compared with a similar past study and also with other well-known evolutionary algorithms.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.031
      Issue No: Vol. 61 (2017)
  • Artificial memory optimization
    • Authors: Guang-qiu Huang
      Pages: 497 - 526
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Guang-qiu Huang
      To solve some complicated optimization problems, an artificial memory optimization (AMO) is constructed based on the human memory mechanism. In AMO, a memory cell is used to trace an alternative solution of a problem to be solved; memorizing and forgetting rules of the human memory mechanism are used to control state transition of each memory cell; the state of a memory cell consists of two components, one is the solution state which associates with an alternative solution being traced; another is the memory state which associates with the memory information resulting from tracing results, where the memory residual value (MRV) is stored; the states of memory cells are divided into three types: instantaneous, short- and long-term memory state, each of which can be strengthened or weakened by accepted stimulus strength. If the solution state of a memory cell has transferred to a good position, its MRV will increase, and then the memory cell is not easily to be forgotten; when the solution state of a memory cell is at sticky state, its MRV will decrease until the memory cell is forgotten; this will effectively prevent invalid iteration. In the course of evolution, a memory cell may strive to evolve from the instantaneous, short-term memory state to long-term memory state, it makes search to be various. Because AMO has 6 operators at the curent version, it has wider adaptability to solve different types of optimization problems. Besides, these operators are automatically dispatched according to their executing efficiency. Results show that AMO possesses of strong search capability and high convergence speed when solving some complicated function optimization problems.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.021
      Issue No: Vol. 61 (2017)
  • Enhancing decision-making flexibility by introducing a new last
           aggregation evaluating approach based on multi-criteria group decision
           making and Pythagorean fuzzy sets
    • Authors: Vahid Mohagheghi; S.Meysam Mousavi; Behnam Vahdani
      Pages: 527 - 535
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Vahid Mohagheghi, S.Meysam Mousavi, Behnam Vahdani
      Uncertainty is an important factor in any decision-making process. Different tools and approaches have been introduced to handle the uncertain environment of group decision making. One of the latest tools in dealing with uncertainty is Pythagorean fuzzy sets (PFSs). These sets extend the concept of intuitionistic fuzzy sets. To show the advantages of these new sets, this paper offers a novel last aggregation group decision-making process for weighting and evaluating. The methodology employs a new approach in computing the weight of decision makers. Moreover, the concept of entropy is applied to address the fuzziness of weights of evaluation criteria in the process. The method develops a new index in ranking the alternatives. Finally, the proposed method is last aggregation, which means it will be more precise in situations with high variations in decision makers’ judgments. To show the applicability of the method, an example from the literature is adopted and solved for internet companies.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.003
      Issue No: Vol. 61 (2017)
  • Project scheduling for minimizing temporary availability cost of rental
           resources and tardiness penalty of activities
    • Authors: Behrouz Afshar-Nadjafi; Mirhossein Basati; Hamidreza Maghsoudlou
      Pages: 536 - 548
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Behrouz Afshar-Nadjafi, Mirhossein Basati, Hamidreza Maghsoudlou
      This paper addresses the resource availability cost problem with rental resources where each activity has a given due date to be completed. In this problem setting, the required resources are temporarily rented to accomplish the corresponding activities where the paid fee for the rental resources depends on duration of their availability. In addition, each activity would be subjected to a tardiness penalty if its finish time surpasses its given due date. A mathematical model is presented for the problem and some features of its solution space are established. Also, a best-performed version of ant colony optimization (ACO) algorithm based on Ant Colony System is developed to tackle this strongly NP-Hard problem. The proposed method consists a new compatible schedule generation scheme, a new resource based heuristic role and an efficient local search. In a comprehensive experimental effort, the proposed parameters-tuned approach is compared with the exact solutions obtained by GAMS on several small-scale instances, while results of a competitive metaheuristic based on Genetic Algorithm are employed to validate the developed ACO algorithm for the large-scale instances. Finally, effectiveness of the proposed ACO is analyzed using statistical tests and the impact of the crucial parameters on the resulting solutions is demonstrated.
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      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.033
      Issue No: Vol. 61 (2017)
  • A new metaheuristic optimization methodology based on fuzzy logic
    • Authors: Margarita Arimatea Díaz-Cortés; Erik Cuevas; Jorge Gálvez; Octavio Camarena
      Pages: 549 - 569
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Margarita Arimatea Díaz-Cortés, Erik Cuevas, Jorge Gálvez, Octavio Camarena
      Many processes are too complex to be manipulated quantitatively; however, humans succeed by using simple rules of thumb that are extracted from their experiences. Fuzzy logic emulates the human reasoning in the use of imprecise information to generate decisions. Unlike traditional approaches, which require a mathematical understanding of the system, fuzzy logic comprises an alternative way of processing, which permits modeling complex systems through the use of human knowledge. On the other hand, several new metaheuristic algorithms have recently been proposed with interesting results. Most of them use operators based on metaphors of natural or social elements to evolve candidate solutions. In this paper, a methodology to implement human-knowledge-based optimization strategies is presented. In the scheme, a Takagi-Sugeno Fuzzy inference system is used to reproduce a specific search strategy generated by a human expert. Therefore, the number of rules and its configuration only depend on the expert experience without considering any learning rule process. Under these conditions, each fuzzy rule represents an expert observation that models the conditions under which candidate solutions are modified in order to reach the optimal location. To exhibit the performance and robustness of the proposed method, a comparison to other well-known optimization methods is conducted. The comparison considers several standard benchmark functions which are typically found in scientific literature. The results suggest a high performance of the proposed methodology.
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      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.038
      Issue No: Vol. 61 (2017)
  • An efficient method for multilevel color image thresholding using cuckoo
           search algorithm based on minimum cross entropy
    • Authors: S. Pare; A. Kumar; V. Bajaj; G.K. Singh
      Pages: 570 - 592
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): S. Pare, A. Kumar, V. Bajaj, G.K. Singh
      Among various thresholding methods, minimum cross entropy is implemented for its effectiveness and simplicity. Although it is efficient and gives excellent result in case of bi-level thresholding, but its evaluation becomes computationally costly when extended to perform multilevel thresholding owing to the exhaustive search performed for the optimum threshold values. Therefore, in this paper, an efficient multilevel thresholding technique based on cuckoo search algorithm is adopted to render multilevel minimum cross entropy more practical and reduce the complexity. Experiments have been conducted over different color images including natural and satellite images exhibiting low resolution, complex backgrounds and poor illumination. The feasibility and efficiency of proposed approach is investigated through an extensive comparison with multilevel minimum cross entropy based methods that are optimized using artificial bee colony, bacterial foraging optimization, differential evolution, and wind driven optimization. In addition, the proposed approach is compared with thresholding techniques depending on between-class variance (Otsu) method and Tsalli’s entropy function. Experimental results based on qualitative results and different fidelity parameters depicts that the proposed approach selects optimum threshold values more efficiently and accurately as compared to other compared techniques and produces high quality of the segmented images.
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      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.039
      Issue No: Vol. 61 (2017)
  • Using genetic algorithm to support clustering-based portfolio optimization
           by investor information
    • Authors: Donghyun Cheong; Young Min Kim; Hyun Woo Byun; Kyong Joo Oh; Tae Yoon Kim
      Pages: 593 - 602
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Donghyun Cheong, Young Min Kim, Hyun Woo Byun, Kyong Joo Oh, Tae Yoon Kim
      A clustering-based portfolio optimization scheme that employs a genetic algorithm (GA) based on investor information for active portfolio management is presented. Whereas numerous studies have investigated trading behaviors, investor performance, and portfolio investment strategies, few works have developed investment strategies based on investor information. This study is conducted in two phases. First, a basket of portfolio (i.e., a collection of stocks held in individual portfolios) is developed through a cluster analysis of investor information. A GA is then employed to optimize the weights of the selected stocks. And the optimized portfolio is rebalanced to get excess return. It is concluded that the proposed multistage portfolio optimization scheme for active portfolio management generates superior results than previously proposed methods for the Korean stock market.
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      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.042
      Issue No: Vol. 61 (2017)
  • A radial space division based evolutionary algorithm for many-objective
    • Authors: Cheng He; Ye Tian; Yaochu Jin; Xingyi Zhang; Linqiang Pan
      Pages: 603 - 621
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Cheng He, Ye Tian, Yaochu Jin, Xingyi Zhang, Linqiang Pan
      In evolutionary many-objective optimization, diversity maintenance plays an important role in pushing the population towards the Pareto optimal front. Existing many-objective evolutionary algorithms mainly focus on convergence enhancement, but pay less attention to diversity enhancement, which may fail to obtain uniformly distributed solutions or fall into local optima. This paper proposes a radial space division based evolutionary algorithm for many-objective optimization, where the solutions in high-dimensional objective space are projected into the grid divided 2-dimensional radial space for diversity maintenance and convergence enhancement. Specifically, the diversity of the population is emphasized by selecting solutions from different grids, where an adaptive penalty based approach is proposed to select a better converged solution from the grid with multiple solutions for convergence enhancement. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems. Experimental results demonstrate the competitiveness of the proposed algorithm in terms of both convergence enhancement and diversity maintenance.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.024
      Issue No: Vol. 61 (2017)
  • Modified differential evolution algorithm for contrast and brightness
           enhancement of satellite images
    • Authors: Shilpa Suresh; Shyam Lal
      Pages: 622 - 641
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Shilpa Suresh, Shyam Lal
      Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified Differential Evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no-reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend.

      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.019
      Issue No: Vol. 61 (2017)
  • Cellular matrix model for parallel combinatorial optimization algorithms
           in Euclidean plane
    • Authors: Hongjian Wang; Abdelkhalek Mansouri; Jean-Charles Créput
      Pages: 642 - 660
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Hongjian Wang, Abdelkhalek Mansouri, Jean-Charles Créput
      We propose a parallel computation model, called cellular matrix model (CMM), to address large-size Euclidean graph matching problems in the plane. The parallel computation takes place by partitioning the plane into a regular grid of cells, each cell being affected to a single processor. Each processor operates on local data, starting from its cell location and extending its search to the neighborhood cells in a spiral search way. In order to deal with large-size problems, memory size and processor number are fixed as O(N), where N is the problem size. Then one key point is that closest point searching in the plane is performed in O(1) expected time for uniform or bounded distribution, for each processor independently. We define a generic loop that models the parallel projection between graphs and their matching, as executed by the many cells at a given level of computation granularity. To illustrate its efficacy and versatility, we apply the CMM, on GPU platforms, to two problems in image processing: superpixel segmentation and stereo matching energy minimization. Firstly, we propose an extended version of the well-known SLIC superpixel segmentation algorithm, which we call SPASM algorithm, by using a parallel 2D self-organizing map instead of k-means algorithm. Secondly, we investigate the idea of distributed variable neighborhood search, and propose a parallel search heuristic, called distributed local search (DLS), for global energy minimization of stereo matching problem. We evaluate the approach with regards to the state-of-the-art graph cut and belief propagation algorithms. For each problem, we argue that the parallel GPU implementation provides new competitive quality/time trade-offs, with substantial acceleration factors as the problem size increases.
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      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.015
      Issue No: Vol. 61 (2017)
  • Handling binary classification problems with a priority class by using
           Support Vector Machines
    • Authors: L. Gonzalez-Abril; C. Angulo; H. Nuñez; Y. Leal
      Pages: 661 - 669
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): L. Gonzalez-Abril, C. Angulo, H. Nuñez, Y. Leal
      A post-processing technique for Support Vector Machine (SVM) algorithms for binary classification problems is introduced in order to obtain adequate accuracy on a priority class (labelled as a positive class). That is, the true positive rate (or recall or sensitivity) is prioritized over the accuracy of the overall classifier. Hence, false negative (or Type I) errors receive greater consideration than false positive (Type II) errors during the construction of the model. This post-processing technique tunes the initial bias term once a solution vector is learned by using standard SVM algorithms in two steps: First, a fixed threshold is given as a lower bound for the recall measure; second, the true negative rate (or specificity) is maximized. Experiments, carried out on eleven standard UCI datasets, show that the modified SVM satisfies the aims for which it has been designed. Furthermore, results are comparable or better than those obtained when other state-of-the-art SVM algorithms and other usual metrics are considered.
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      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.023
      Issue No: Vol. 61 (2017)
  • A checkpointed league championship algorithm-based cloud scheduling scheme
           with secure fault tolerance responsiveness
    • Authors: Shafi’i Muhammad Abdulhamid; Muhammad Shafie Abd Latiff
      Pages: 670 - 680
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Shafi’i Muhammad Abdulhamid, Muhammad Shafie Abd Latiff
      In its simplest structure, cloud computing technology is a massive collection of connected servers residing in a datacenter and continuously changing to provide services to users on-demand through a front-end interface. The failure of task during execution is no more an accident but a frequent attribute of scheduling systems in a large-scale distributed environment. Recently, some computational intelligence techniques have been mostly utilized to decipher the problems of scheduling in the cloud environment, but only a few emphasis on the issue of fault tolerance. This research paper puts forward a Checkpointed League Championship Algorithm (CPLCA) scheduling scheme to be used in the cloud computing system. It is a fault-tolerance aware task scheduling mechanisms using the checkpointing strategy in addition to tasks migration against unexpected independent task execution failure. The simulation results show that, the proposed CPLCA scheme produces an improvement of 41%, 33% and 23% as compared with the Ant Colony Optimization (ACO), Genetic Algorithm (GA) and the basic league championship algorithm (LCA) respectively as parametrically measured using the total average makespan of the schemes. Considering the total average response time of the schemes, the CPLCA scheme produces an improvement of 54%, 57% and 30% as compared with ACO, GA and LCA respectively. It also turns out significant failure decrease in jobs execution as measured in terms of failure metrics and performance improvement rate. From the results obtained, CPLCA provides an improvement in both tasks scheduling performance and failure awareness that is more appropriate for scheduling in the cloud computing model.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.048
      Issue No: Vol. 61 (2017)
  • A modified symbiotic organisms search (mSOS) algorithm for optimization of
           pin-jointed structures
    • Authors: Dieu T.T. Do; Jaehong Lee
      Pages: 683 - 699
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Dieu T.T. Do, Jaehong Lee
      The paper introduces a modified symbiotic organisms search (mSOS) algorithm to optimization of pin-jointed structures including truss and tensegrity ones. This approach is refined from the original SOS with five modifications in the following three phases: mutualism, commensalism and parasitism. In the mutualism one, benefit factors are suggested as 1 to equally represent the level of benefit to each organism, whilst the best organism is replaced by a randomly selected one to increase the global search capability. With the aim of improving the convergence speed, randomly created coefficients in the commensalism phase are restricted in the range [0.4, 0.9]. Additionally, an elitist technique is applied to this phase to filter the best organisms for the next generation as well. Finally, the parasitism phase is eliminated to simplify the implementation and reduce the time-consuming process. To verify the effectiveness and robustness of the proposed algorithm, five examples relating to truss weight minimization with discrete design variables are performed. Additionally, two examples regarding minimization a function of eigenvalues and force densities of tensegrity structures with continuous design variables are considered further. Optimal results acquired in all illustrated examples reveal that the proposed method requires fewer number of analyses than the original SOS and the DE, but still gaining high-quality solutions. Furthermore, the mSOS also outperforms numerous other algorithms in available literature in terms of optimal solutions, especially for problems with a large number of design variables.
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      PubDate: 2017-09-07T13:28:44Z
      DOI: 10.1016/j.asoc.2017.08.002
      Issue No: Vol. 61 (2017)
  • Multilayer feed forward neural networks for non-linear continuous
           bidirectional associative memory
    • Authors: Manu Pratap Singh; V.K. Saraswat
      Pages: 700 - 713
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Manu Pratap Singh, V.K. Saraswat
      The study of Bidirectional associative memory (BAM), with recurrent neural networks and symmetric as well as asymmetric weights, has already been undertaken in various different ways. Using two phases of learning for multilayer neural network architecture in the present paper, a multilayer feed forward neural network model has been proposed to construct the non-linear continuous BAM for pattern association. In the first phase an input pattern is presented to input layer and back propagation learning rule is used to train the network in the feed forward direction for the corresponding associated output pattern. In second phase the output pattern is presented to output layer as input and again the back propagation learning rule is used to train the same network in feedback direction for the corresponding associated input pattern. In these two passes i.e. forward pass and backward pass, the interconnection weights are considered asymmetric. This training process continues till the network does not converge to the final optimal weights by minimizing the mean square errors in both the directions simultaneously. At this convergence of weights the input and output layers exhibit the stability and the performance of such type of BAM is evaluated for the test pattern set while the simulation results exhibit the better performance of associative memory for the proposed method. The storage capacity of network is also increased due to the non-linear mapping between input-output pattern pairs and also the occurrence of spurious states reduces during the recalling process.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.026
      Issue No: Vol. 61 (2017)
  • A modified two-part wolf pack search algorithm for the multiple traveling
           salesmen problem
    • Authors: Yongbo Chen; Zhenyue Jia; Xiaolin Ai; Di Yang; Jianqiao Yu
      Pages: 714 - 725
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Yongbo Chen, Zhenyue Jia, Xiaolin Ai, Di Yang, Jianqiao Yu
      This paper proposes a modified two-part wolf pack search (MTWPS) algorithm updated by the two-part individual encoding approach as well as the transposition and extension (TE) operation for the multiple travelling salesmen problem (MTSP). Firstly, the two-part individual encoding approach is introduced into the original WPS algorithm for MTSP, which is named the two-part wolf pack search (TWPS) algorithm, to minimize the size of the problem search space. Secondly, the analysis of the convergence rate performance is presented to illustrate the reasonability of the maximum terminal generation of the novel TWPS algorithm deeply. Then, based on the definition of the global reachability, the TWPS algorithm is modified by the TE operation further, which can greatly enhance the search ability of the TWPS algorithm. Finally, focusing on the objective of minimizing the total travel distance and the longest tour, comparisons of the robustness and the optimality between different algorithms are presented, and experimental results show that the MTWPS algorithm can obtain higher solution quality than the other the ones of the other two methods
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.041
      Issue No: Vol. 61 (2017)
  • A hybrid swarm algorithm based on ABC and AIS for 2L-HFCVRP
    • Authors: Defu Zhang; Ruibing Dong; Yain-Whar Si; Furong Ye; Qisen Cai
      First page: 726
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Defu Zhang, Ruibing Dong, Yain-Whar Si, Furong Ye, Qisen Cai
      This paper mainly addresses the heterogeneous fleet capacitated vehicle routing problem with two-dimensional loading constrains (2L-HFCVRP). The 2L-HFCVRP is a combination of two NP-hard problems and has a wide range of applications in transportation and logistics fields. In this paper, we propose a hybrid swarm algorithm, which is a combination of Artificial Bee Colony (ABC) algorithm and Artificial Immune System (AIS) algorithm, to solve the 2L-HFCVRP. The proposed algorithm is allowed to search infeasible solutions and several efficient strategies are developed to escape from local optima. The extensive computational results on several well-known benchmark data sets verify the effectiveness of the proposed algorithm. The proposed algorithm is shown to outperform the best algorithms in the literature for 2L-HFCVRP instances.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.045
      Issue No: Vol. 61 (2017)
  • Hesitant fuzzy decision tree approach for highly imbalanced data
    • Authors: Sahar Sardari; Mahdi Eftekhari; Fatemeh Afsari
      Pages: 727 - 741
      Abstract: Publication date: December 2017
      Source:Applied Soft Computing, Volume 61
      Author(s): Sahar Sardari, Mahdi Eftekhari, Fatemeh Afsari
      Fuzzy decision tree algorithms provide one of the most powerful classifiers applied to any kind of data. In this paper, some new Fuzzy Decision Tree (FDT) approaches based on Hesitant Fuzzy Sets (HFSs) have been introduced to classify highly imbalanced data sets. Our proposed classifiers employ k-means clustering algorithm to divide the majority class samples into several clusters. Then, each cluster sample is labeled by a new synthetic class label. After that, five discretization methods (Fayyad, Fusinter, Fixed Frequency, Proportional, and Uniform Frequency) are considered to generate Membership Functions (MFs) of each attribute. Five FDTs are constructed based on five discretization methods Hesitant Fuzzy Information Gain (HFIG) is proposed as a new attribute selection criterion that can be used instead of Fuzzy Information Gain (FIG). HFIG is calculated by aggregating obtained FIGs from different discretization methods by information energy. For predicting the class label of new samples, three aggregation methods are utilized. The combination of splitting criterion (HFIG or FIG), five different discretization methods (for generating MFs) and three aggregation methods (to predict class label of new samples) generate special classifiers for addressing the imbalanced classification. For illustrating the difference between our proposed methods, taxonomy is proposed in the paper that categorizes them in three general categories. The experimental results show that our proposed methods outperform the other fuzzy rule-based approaches over 20 highly imbalanced data sets of KEEL in terms of AUC.
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      PubDate: 2017-09-14T08:48:05Z
      DOI: 10.1016/j.asoc.2017.08.052
      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
School of Mathematical and Computer Sciences
Heriot-Watt University
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