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  Subjects -> COMPUTER SCIENCE (Total: 1969 journals)
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COMPUTER SCIENCE (1147 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: 11)
Abakós     Open Access   (Followers: 3)
Academy of Information and Management Sciences Journal     Full-text available via subscription   (Followers: 67)
ACM Computing Surveys     Hybrid Journal   (Followers: 23)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 8)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 11)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 12)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 3)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 19)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 25)
Advanced Science Letters     Full-text available via subscription   (Followers: 5)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 14)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 53)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 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: 35)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 2)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 5)
Analysis in Theory and Applications     Hybrid Journal  
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 12)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 2)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 31)
Applied Medical Informatics     Open Access   (Followers: 9)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 16)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 8)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 3)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 231)
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: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 117)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 12)
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)
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: 13)
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: 18)
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: 47)
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  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 1)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 12)
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: 27)
Computer     Full-text available via subscription   (Followers: 78)
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: 8)
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: 13)
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: 10)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (Followers: 9)
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  [3031 journals]
  • Linguistic value soft set-based approach to multiple criteria group
           decision-making
    • Authors: Bingzhen Sun; Weimin Ma; Xiaonan Li
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Bingzhen Sun, Weimin Ma, Xiaonan Li
      Molodtsov's soft set theory provides a general mathematical tool for dealing with uncertainty in decision-making. In the past few years, a large number of generalized soft set concepts and approaches to decision-making have been studied by many researchers. This paper considers a new generalization of Molodtsov's soft set concept, named linguistic value soft set, by introducing a linguistic variable to the definition of soft set theory. Then, the information in several soft sets can be saved in one linguistic value soft set. We define basic notions such as the equality of two linguistic value soft sets, and the subsets and complement of a linguistic soft set. Next, we present the binary operations for linguistic value soft set theory. Then, we discuss the properties of linguistic value soft set for the basic notions and binary operations. Further, we define the concept of linguistic value soft matrix and choice value matrix of a linguistic value soft set, and we discuss the operation laws. By using these new definitions of linguistic value soft set theory, we establish a new approach to multiple criteria group decision-making problems with linguistic value information. We present a detailed description of the decision-making problem and the decision steps. Finally, the validity of the proposed decision-making method is tested by a numerical example with the background of an evaluation decision problem for automobiles. The main contribution of this paper is twofold. One is to propose the concept of linguistic value soft set and establish the main results of the theoretical aspect for the new generalization form. Another is to present a new approach to multiple attribute group decision-making based on linguistic value soft set.

      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.03.033
      Issue No: Vol. 58 (2017)
       
  • Fractional order fuzzy-PID control of a combined cycle power plant using
           Particle Swarm Optimization algorithm with an improved dynamic parameters
           selection
    • Authors: V. Haji Haji; Concepción A. Monje
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): V. Haji Haji, Concepción A. Monje
      The effectiveness of the Particle Swarm Optimization (PSO) algorithm in solving any optimization problem is highly dependent on the right selection of tuning parameters. A better control parameter improves the flexibility and robustness of the algorithm. In this paper, a new PSO algorithm based on dynamic control parameters selection is presented in order to further enhance the algorithm's rate of convergence and the minimization of the fitness function. The powerful Dynamic PSO (DPSO) uses a new mechanism to dynamically select the best performing combinations of acceleration coefficients, inertia weight, and population size. A fractional order fuzzy-PID (fuzzy-FOPID) controller based on the DPSO algorithm is proposed to perform the optimization task of the controller gains and improve the performance of a single-shaft Combined Cycle Power Plant (CCPP). The proposed controller is used in speed control loop to improve the response during frequency drop or change in loading. The performance of the fuzzy-FOPID based DPSO is compared with those of the conventional PSO, Comprehensive Learning PSO (CLPSO), Heterogeneous CLPSO (HCLPSO), Genetic Algorithm (GA), Differential Evolution (DE), and Artificial Bee Colony (ABC) algorithm. The simulation results show the effectiveness and performance of the proposed method for frequency drop or change in loading.
      Graphical abstract image

      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.04.033
      Issue No: Vol. 58 (2017)
       
  • Hybrid system of ART and RBF neural networks for online clustering
    • Authors: Andrzej Bielecki; Mateusz Wójcik
      Pages: 1 - 10
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Andrzej Bielecki, Mateusz Wójcik
      An online clustering task is considered for machine state monitoring purpose. In the previous authors’ researches a classical ART-2 network was tested for online classification of operational states in the context of a wind turbine monitoring. Some drawbacks, however, were found when a data stream size had been increased. This case is investigated in this paper. Classical ART-2 network can cluster data incorrectly when data points are compared by using Euclidean distance. Furthermore, ART-2 network can lose accuracy when data stream is processed for long time. The way of improving the ART-2 network is considered and two main steps of that are taken. At first, the stereographic projection is implemented. At the second step, a new type of hybrid neural system which consists of ART-2 and RBF networks with data processed by using the stereographic projection is introduced. Tests contained elementary scenarios for low-dimensional cases as well as higher dimensional real data from wind turbine monitoring. All the tests implied that an efficient system for online clustering had been found.
      Graphical abstract image Highlights

      PubDate: 2017-05-07T18:44:26Z
      DOI: 10.1016/j.asoc.2017.04.012
      Issue No: Vol. 58 (2017)
       
  • Hybrid Artificial Bee Colony algorithm with Differential Evolution
    • Authors: Shimpi Singh Jadon; Ritu Tiwari; Harish Sharma; Jagdish Chand Bansal
      Pages: 11 - 24
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Shimpi Singh Jadon, Ritu Tiwari, Harish Sharma, Jagdish Chand Bansal
      Artificial Bee Colony (ABC) and Differential Evolution (DE) are two very popular and efficient meta-heuristic algorithms. However, both algorithms have been applied to various science and engineering optimization problems, extensively, the algorithms suffer from premature convergence, unbalanced exploration-exploitation, and sometimes slow convergence speed. Hybridization of ABC and DE may provide a platform for developing a meta-heuristic algorithm with better convergence speed and a better balance between exploration and exploitation capabilities. This paper proposes a hybridization of ABC and DE algorithms to develop a more efficient meta-heuristic algorithm than ABC and DE. In the proposed hybrid algorithm, Hybrid Artificial Bee Colony with Differential Evolution (HABCDE), the onlooker bee phase of ABC is inspired from DE. Employed bee phase is modified by employing the concept of the best individual while scout bee phase has also been modified for higher exploration. The proposed HABCDE has been tested over 20 test problems and 4 real-world optimization problems. The performance of HABCDE is compared with the basic version of ABC and DE. The results are also compared with state-of-the-art algorithms, namely Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO) and Spider Monkey Optimization (SMO) to establish the superiority of the proposed algorithm. For further validation of the proposed hybridization, the experimental results are also compared with other hybrid versions of ABC and DE, namely ABC-DE, DE-BCO and HDABCA and with modified ABC algorithms, namely Best-So-Far ABC (BSFABC), Gbest guided ABC (GABC) and modified ABC (MABC). Results indicate that HABCDE would be a competitive algorithm in the field of meta-heuristics.
      Graphical abstract image Highlights

      PubDate: 2017-05-07T18:44:26Z
      DOI: 10.1016/j.asoc.2017.04.018
      Issue No: Vol. 58 (2017)
       
  • On the effect of reference point in MOEA/D for multi-objective
           optimization
    • Authors: Rui Wang; Jian Xiong; Hisao Ishibuchi; Guohua Wu; Tao Zhang
      Pages: 25 - 34
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Rui Wang, Jian Xiong, Hisao Ishibuchi, Guohua Wu, Tao Zhang
      Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has continuously proven effective for multi-objective optimization. So far, the effect of weight vectors and scalarizing methods in MOEA/D has been intensively studied. However, the reference point which serves as the starting point of reference lines (determined by weight vectors) is yet to be well studied. This study aims to fill in this research gap. Ideally, the ideal point of a multi-objective problem could serve as the reference point, however, since the ideal point is often unknown beforehand, the reference point has to be estimated (or specified). In this study, the effect of the reference point specified in three representative manners, i.e., pessimistic, optimistic and dynamic (from optimistic to pessimistic), is examined on three sets of benchmark problems. Each set of the problems has different degrees of difficulty in convergence and spread. Experimental results show that (i) the reference point implicitly impacts the convergence and spread performance of MOEA/D; (ii) the pessimistic specification emphasizes more of exploiting existing regions and the optimistic specification emphasizes more of exploring new regions; (iii) the dynamic specification can strike a good balance between exploitation and exploration, exhibiting good performance for most of the test problems, and thus, is commended to use for new problems.
      Graphical abstract image Highlights This figure illustrates the main idea of this paper. That is, the reference point specified in three representative ways, i.e., optimistic, pessimistic and dynamic leads to different performances for decomposition based algorithms. The dynamic way is demonstrated as the most robust for a range of benchmarks.

      PubDate: 2017-05-07T18:44:26Z
      DOI: 10.1016/j.asoc.2017.04.002
      Issue No: Vol. 58 (2017)
       
  • Forecasting financial time series volatility using Particle Swarm
           Optimization trained Quantile Regression Neural Network
    • Authors: Dadabada Pradeepkumar; Vadlamani Ravi
      Pages: 35 - 52
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Dadabada Pradeepkumar, Vadlamani Ravi
      Accurate forecasting of volatility from financial time series is paramount in financial decision making. This paper presents a novel, Particle Swarm Optimization (PSO)-trained Quantile Regression Neural Network namely PSOQRNN, to forecast volatility from financial time series. We compared the effectiveness of PSOQRNN with that of the traditional volatility forecasting models, i.e., Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and three Artificial Neural Networks (ANNs) including Multi-Layer Perceptron (MLP), General Regression Neural Network (GRNN), Group Method of Data Handling (GMDH), Random Forest (RF) and two Quantile Regression (QR)-based hybrids including Quantile Regression Neural Network (QRNN) and Quantile Regression Random Forest (QRRF). The results indicate that the proposed PSOQRNN outperformed these models in terms of Mean Squared Error (MSE), on a majority of the eight financial time series including exchange rates of USD versus JPY, GBP, EUR and INR, Gold Price, Crude Oil Price, Standard and Poor 500 (S&P 500) Stock Index and NSE India Stock Index considered here. It was corroborated by the Diebold–Mariano test of statistical significance. It also performed well in terms of other important measures such as Directional Change Statistic (Dstat) and Theil's Inequality Coefficient. The superior performance of PSOQRNN can be attributed to the role played by PSO in obtaining the better solutions. Therefore, we conclude that the proposed PSOQRNN can be used as a viable alternative in forecasting volatility.
      Graphical abstract image Highlights

      PubDate: 2017-05-07T18:44:26Z
      DOI: 10.1016/j.asoc.2017.04.014
      Issue No: Vol. 58 (2017)
       
  • Automatic feature extraction of time-series applied to fault severity
           assessment of helical gearbox in stationary and non-stationary speed
           operation
    • Authors: Diego Cabrera; Fernando Sancho; Chuan Li; Mariela Cerrada; René-Vinicio Sánchez; Fannia Pacheco; José Valente de Oliveira
      Pages: 53 - 64
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Diego Cabrera, Fernando Sancho, Chuan Li, Mariela Cerrada, René-Vinicio Sánchez, Fannia Pacheco, José Valente de Oliveira
      Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods.
      Graphical abstract image Highlights

      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.016
      Issue No: Vol. 58 (2017)
       
  • Quadratic-radial-basis-function-kernel for classifying multi-class
           agricultural datasets with continuous attributes
    • Authors: K. Aditya Shastry; H.A. Sanjay; G. Deexith
      Pages: 65 - 74
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): K. Aditya Shastry, H.A. Sanjay, G. Deexith
      Classification of agricultural data such as soil data and crop data is significant as it allows the stakeholders to make meaningful decisions for farming. Soil classification aids farmers in deciding the type of crop to be sown for a particular type of soil. Similarly, wheat variety classification assists in selecting the right type of wheat for a particular product. Current methods used for classifying agricultural data are mostly manual. These methods involve agriculture field visits and surveys and are labor-intensive, expensive, and prone to human error. Recently, data mining techniques such as decision trees, k-nearest neighbors (k-NN), support vector machine (SVM), and Naive Bayes (NB) have been used in classification of agricultural data such as soil, crops, and land cover. The resulting classification aid the decision making process of government organizations and agro-industries in the field of agriculture. SVM is a popular approach for data classification. A recent study on SVM highlighted the fact that using multiple kernels instead of a single kernel would lead to better performance because of the greater learning and generalization power. In this work, a hybrid kernel based support vector machine (H-SVM) is proposed for classifying multi-class agricultural datasets having continuous attributes. Genetic algorithm (GA) or gradient descent (GD) methods are utilized to select the SVM parameters C and γ. The proposed kernel is called the quadratic-radial-basis-function kernel (QRK) and it combines both quadratic and radial basis function (RBF) kernels. The proposed classifier has the ability to classify all kinds of multi-class agricultural datasets with continuous features. Rigorous experiments using the proposed method are performed on standard benchmark and real world agriculture datasets. The results reveal a significant performance improvement over state of the art methods such as NB, k-NN, and SVM in terms of performance metrics such as accuracy, sensitivity, specificity, precision, and F-score.
      Graphical abstract image Highlights

      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.049
      Issue No: Vol. 58 (2017)
       
  • Considering diversity and accuracy simultaneously for ensemble pruning
    • Authors: Qun Dai; Rui Ye; Zhuan Liu
      Pages: 75 - 91
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Qun Dai, Rui Ye, Zhuan Liu
      Diversity among individual classifiers is widely recognized to be a key factor to successful ensemble selection, while the ultimate goal of ensemble pruning is to improve its predictive accuracy. Diversity and accuracy are two important properties of an ensemble. Existing ensemble pruning methods always consider diversity and accuracy separately. However, in contrast, the two closely interrelate with each other, and should be considered simultaneously. Accordingly, three new measures, i.e., Simultaneous Diversity & Accuracy, Diversity-Focused-Two and Accuracy-Reinforcement, are developed for pruning the ensemble by greedy algorithm. The motivation for Simultaneous Diversity & Accuracy is to consider the difference between the subensemble and the candidate classifier, and simultaneously, to consider the accuracy of both of them. With Simultaneous Diversity & Accuracy, those difficult samples are not given up so as to further improve the generalization performance of the ensemble. The inspiration of devising Diversity-Focused-Two stems from the cognition that ensemble diversity attaches more importance to the difference among the classifiers in an ensemble. Finally, the proposal of Accuracy-Reinforcement reinforces the concern about ensemble accuracy. Extensive experiments verified the effectiveness and efficiency of the proposed three pruning measures. Through the investigation of this work, it is found that by considering diversity and accuracy simultaneously for ensemble pruning, well-performed selective ensemble with superior generalization capability can be acquired, which is the scientific value of this paper.
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      PubDate: 2017-05-07T18:44:26Z
      DOI: 10.1016/j.asoc.2017.04.058
      Issue No: Vol. 58 (2017)
       
  • ARMA(p,q) type high order fuzzy time series forecast method based on fuzzy
           logic relations
    • Authors: Cem Kocak
      Pages: 92 - 103
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Cem Kocak
      Within classic time series approaches, a time series model can be studied under 3 groups, namely AR (autoregressive model), MA (moving averages model) and ARMA (autoregressive moving averages model). On the other hand, solutions are based mostly on fuzzy AR time series models in the fuzzy time series literature. However, just a few fuzzy ARMA time series models have proposed until now. Fuzzy AR time series models have been divided into two groups named first order and high order models in the literature, highlighting the impact of model degree on forecast performance. However, model structure has been disregarded in these fuzzy AR models. Therefore, it is necessary to eliminate the model specification error arising from not utilizing of MA variables in the fuzzy time series approaches. For this reason, a new high order fuzzy ARMA(p,q) time series solution algorithm based on fuzzy logic group relations including fuzzy MA variables along with fuzzy AR variables has been proposed in this study. The main purpose of this article is to show that the forecast performance can be significantly improved when the deficiency of not utilizing MA variables. The other aim is also to show that the proposed method is better than the other fuzzy ARMA time series models in the literature from the point of forecast performance. Therefore, the new proposed method has been compared regarding forecast performance against some methods commonly used in literature by applying them on gold prices in Turkey, Istanbul Stock Exchange (IMKB) and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX).
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      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.021
      Issue No: Vol. 58 (2017)
       
  • A hybrid algorithm combining glowworm swarm optimization and complete
           2-opt algorithm for spherical travelling salesman problems
    • Authors: Xin Chen; Yongquan Zhou; Zhonghua Tang; Qifang Luo
      Pages: 104 - 114
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Xin Chen, Yongquan Zhou, Zhonghua Tang, Qifang Luo
      The Travelling Salesman Problem (TSP) is one of the most well-known combinatorial optimization problems and has attracted a lot of interests from researchers. Many studies have proposed various methods for solving the two-dimensional TSP. In this study, we extend the two-dimensional TSP to the three-dimensional TSP, namely the spherical TSP in which all points (cities) and paths (solutions) are on the surface of a sphere. A hybrid algorithm based on the glowworm swarm optimization (GSO) and the complete 2-opt algorithm is proposed, in which the carriers of the luciferin are transformed from glowworms to edges between cities, and the probabilistic formula and the luciferin updating formula are modified. In addition, the complete 2-opt algorithm is performed to optimize the selected optimal routes every few iterations. Numerical experimental results show that the proposed algorithm has a better performance than the basic GSO in solving the spherical TSP. Meanwhile, the complete 2-opt algorithm can speed up the convergence rate.

      PubDate: 2017-05-07T18:44:26Z
      DOI: 10.1016/j.asoc.2017.04.057
      Issue No: Vol. 58 (2017)
       
  • Entropic simplified swarm optimization for the task assignment problem
    • Authors: Chyh-Ming Lai; Wei-Chang Yeh; Yen-Cheng Huang
      Pages: 115 - 127
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Chyh-Ming Lai, Wei-Chang Yeh, Yen-Cheng Huang
      The task assignment problem (TAP) aims to assign application tasks to a number of distributed processors in a computation system in order to increase the efficiency and effectiveness of the system for minimizing or maximizing a certain cost. The problem is NP-hard; thus, finding the exact solutions is computationally intractable for larger size problems. In this paper, a novel entropic simplified swarm optimization, known as ESSO, is proposed for solving this problem. In this method, an entropic local search (ELS) inspired by information theory is proposed to enhance the exploitation capability of SSO. Entropy is adopted to describe the uncertainty level of assigned tasks; the task with higher uncertainty then has more chance to be reassigned. Furthermore, for each reassigned task, the corresponding list of potential processors can be constructed using information theory; this enhances the probability of finding promising solutions in ELS. To empirically evaluate the performance of the proposed method, experiments are conducted using twenty-four randomly generated problems ranging from small to large scale, and the corresponding results are compared with existing works. The experiment results indicate that ESSO is better than its competitors in both solution quality and efficiency.

      PubDate: 2017-05-07T18:44:26Z
      DOI: 10.1016/j.asoc.2017.04.030
      Issue No: Vol. 58 (2017)
       
  • Water cycle algorithm-based economic dispatcher for sequential and
           simultaneous objectives including practical constraints
    • Authors: M.A. Elhameed; A.A. El-Fergany
      Pages: 145 - 154
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): M.A. Elhameed, A.A. El-Fergany
      The article presents an efficient methodology based-on water cycle algorithm (WCA) to solve single and multiple objectives of economic load dispatch (ELD) aiming to generate the optimal value of the active generated power for each unit. Three objectives are adopted for optimisation either sequentially or concurrently; they are: (i) fuel cost considering valve-ripple effect, (ii) emission rate, and (iii) total network loss. The generating unit prohibited zones along with ramp rate limits and generating unit power limits specify the inequality constraints of the problem while maintaining system power balance. Usually, optimisation of simultaneous multiple objectives produces set of non-dominated Pareto-front solutions. To help the decision maker, the best compromise solution is carefully picked among optimal Pareto-front points. The proposed WCA-based methodology is demonstrated on three test cases with various complexities and under number of objective scenarios. Numerical results and further subsequent comparisons to other challenging optimisers indicate the viability and confirm the strength of the proposed WCA-based ELD method.
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      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.04.046
      Issue No: Vol. 58 (2017)
       
  • A BPSO-SVM algorithm based on memory renewal and enhanced mutation
           mechanisms for feature selection
    • Authors: Jiaxuan Wei; Ruisheng Zhang; Zhixuan Yu; Rongjing Hu; Jianxin Tang; Chun Gui; Yongna Yuan
      Pages: 176 - 192
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Jiaxuan Wei, Ruisheng Zhang, Zhixuan Yu, Rongjing Hu, Jianxin Tang, Chun Gui, Yongna Yuan
      Feature selection (FS) is an essential component of data mining and machine learning. Most researchers devoted to get more effective method with high accuracy and fewer features, it has become one of the most challenging problems in FS. Certainly, some algorithms have been proven to be effectively, such as binary particle swarm optimization (BPSO), genetic algorithm (GA) and support vector machine (SVM). BPSO is a metaheuristic algorithm having been widely applied to various fields and applications successfully, including FS. As a wrapper method of FS, BPSO-SVM tends to be trapped into premature easily. In this paper, we present a novel mutation enhanced BPSO-SVM algorithm by adjusting the memory of local and global optimum (LGO) and increasing the particles’ mutation probability for feature selection to overcome convergence premature problem and achieve high quality features. Typical simulated experimental results carried out on Sonar, LSVT and DLBCL datasets indicated that the proposed algorithm improved the accuracy and decreased the number of feature subsets, comparing with existing modified BPSO algorithms and GA.
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      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.061
      Issue No: Vol. 58 (2017)
       
  • Modified frequency-based term weighting schemes for text classification
    • Authors: Thabit Sabbah; Ali Selamat; Md Hafiz Selamat; Fawaz S. Al-Anzi; Enrique Herrera Viedma; Ondrej Krejcar; Hamido Fujita
      Pages: 193 - 206
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Thabit Sabbah, Ali Selamat, Md Hafiz Selamat, Fawaz S. Al-Anzi, Enrique Herrera Viedma, Ondrej Krejcar, Hamido Fujita
      With the rapid growth of textual content on the Internet, automatic text categorization is a comparatively more effective solution in information organization and knowledge management. Feature selection, one of the basic phases in statistical-based text categorization, crucially depends on the term weighting methods In order to improve the performance of text categorization, this paper proposes four modified frequency-based term weighting schemes namely; mTF, mTFIDF, TFmIDF, and mTFmIDF. The proposed term weighting schemes take the amount of missing terms into account calculating the weight of existing terms. The proposed schemes show the highest performance for a SVM classifier with a micro-average F1 classification performance value of 97%. Moreover, benchmarking results on Reuters-21578, 20Newsgroups, and WebKB text-classification datasets, using different classifying algorithms such as SVM and KNN show that the proposed schemes mTF, mTFIDF, and mTFmIDF outperform other weighting schemes such as TF, TFIDF, and Entropy. Additionally, the statistical significance tests show a significant enhancement of the classification performance based on the modified schemes.

      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.069
      Issue No: Vol. 58 (2017)
       
  • Short-term hydrothermal generation scheduling using improved predator
           influenced civilized swarm optimization technique
    • Authors: Nitin Narang
      Pages: 207 - 224
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Nitin Narang
      An improved predator influenced civilized swarm optimization (IPCSO) technique is proposed to solve short-term conventional hydro-thermal generation scheduling (HTGS) and profit-based HTGS (PB-HTGS) problems. In the proposed IPCSO technique, prey swarm is divided into the number of societies and each society is influenced by its own predator’s effect. In every society, prey particles interact with each other and the best performing prey particle acts as a society leader. The predator particle chases the society leader and society leader tries to escape from it. In this process, predator effect improves the exploitation capability of the algorithm by searching around the respective society leader. Further, society leader of each society interacts with each other and helps to improve the performance of society leaders. The best performing society leader becomes the leader of civilization. For HTGS problem, a multi-chain cascaded hydro model is undertaken along with consideration of water transport delay between reservoirs. The problem is formulated with due consideration of thermal unit valve point effect, prohibited operating zones on reservoir discharge rate and ramp rate limits on thermal unit power generation. The technique is tested on three HTGS systems and one PB-HTGS system. The obtained results have been compared with the results reported in the literature and found satisfactory. The statistical analysis of the results is carried out to verify the robustness of the proposed technique. Further, a nonparametric test is also applied to compare the performance of the proposed technique.
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      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.065
      Issue No: Vol. 58 (2017)
       
  • Software design patterns classification and selection using text
           categorization approach
    • Authors: Shahid Hussain; Jacky Keung; Arif Ali Khan
      Pages: 225 - 244
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Shahid Hussain, Jacky Keung, Arif Ali Khan
      Context Numerous software design patterns have been introduced and cataloged either as a canonical or a variant solution to solve a design problem. The existing automatic techniques for design pattern(s) selection aid novice software developers to select the more appropriate design pattern(s) from the list of applicable patterns to solve a design problem in the designing phase of software development life cycle. Goal However, the existing automatic techniques are limited to the semi-formal specification, multi-class problem, an adequate sample size to make precise learning and individual classifier training in order to determine a candidate design pattern class and suggest more appropriate pattern(s). Method To address these issues, we exploit a text categorization based approach via Fuzzy c-means (unsupervised learning technique) that targets to present a systematic way to group the similar design patterns and suggest the appropriate design pattern(s) to developers related to the specification of a given design problem. We also propose an evaluation model to assess the effectiveness of the proposed approach in the context of several real design problems and design pattern collections. Subsequently, we also propose a new feature selection method Ensemble-IG to overcome the multi-class problem and improve the classification performance of the proposed approach. Results The promising experimental results suggest the applicability of the proposed approach in the domain of classification and selection of appropriate design patterns. Subsequently, we also observed the significant improvement in learning precision of the proposed approach through Ensemble-IG. Conclusion The proposed approach has four advantages as compared to previous work. First, the semi-formal specification of design patterns is not required as a prerequisite; second, the ground reality of class label assignment is not mandatory; third, lack of classifier’s training for each design pattern class and fourth, an adequate sample size is not required to make precise learning.
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      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.043
      Issue No: Vol. 58 (2017)
       
  • Semi-supervised classification by discriminative regularization
    • Authors: Jun Wang; Guangjun Yao; Guoxian Yu
      Pages: 245 - 255
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Jun Wang, Guangjun Yao, Guoxian Yu
      One basic assumption in graph-based semi-supervised classification is manifold assumption, which assumes nearby samples should have similar outputs (or labels). However, manifold assumption may not always hold for samples lying nearby but across the boundary of different classes. As a consequence, samples close to the boundary are quite likely to be misclassified. In this paper, we introduce an approach called semi-supervised classification by discriminative regularization (SSCDR for short) to address this problem. SSCDR first constructs a k nearest neighborhood graph to capture the local manifold structure of samples, and a discriminative graph to encode the discriminative information derived from constrained clustering on labeled and unlabeled samples. Next, it separately treats the discriminative graph and the neighborhood graph in a discriminative regularization framework for semi-supervised classification, and forces nearby samples across the boundary to have different labels. Experimental results on various datasets collected from UCI, LibSVM and facial image datasets demonstrate that SSCDR achieves better performance than other related methods, and it is also robust to the input values of parameter k.
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      PubDate: 2017-05-13T08:04:53Z
      DOI: 10.1016/j.asoc.2017.04.041
      Issue No: Vol. 58 (2017)
       
  • A consensus model for hesitant fuzzy preference relations and its
           application in water allocation management
    • Authors: Yejun Xu; Francisco Javier Cabrerizo; Enrique Herrera-Viedma
      Pages: 265 - 284
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Yejun Xu, Francisco Javier Cabrerizo, Enrique Herrera-Viedma
      This paper investigates a consensus model for hesitant fuzzy preference relations (HFPRs). First, we present a revised definition of HFPRs, in which the values are not ordered for the hesitant fuzzy element. Second, we propose an additive consistency based estimation measure to normalize the HFPRs, based on which, a consensus model is developed. Here, two feedback mechanisms are proposed, namely, interactive mechanism and automatic mechanism, to obtain a solution with desired consistency and consensus levels. In the interactive mechanism, the experts are suggested to give their new preference values in a specific range. If the experts are unwilling to offer their updated preferences, the automatic mechanism could be adopted to carry out the consensus process. Induced ordered weighted averaging (IOWA) operator is used to aggregate the individual HFPRs into a collective one. A score HFPR is proposed for collective HFPR, and then the quantifier-guided dominance degrees of alternatives by using an OWA operator are obtained to rank the alternatives. Finally, both a case of study for water allocation management in Jiangxi Province of China and a comparison with the existing approaches are carried out to show the advantages of the proposed method.
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      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.04.068
      Issue No: Vol. 58 (2017)
       
  • Incorporation of prior knowledge in neural network model for continuous
           cooling of steel using genetic algorithm
    • Authors: Subhamita Chakraborty; Partha P. Chattopadhyay; Swarup K. Ghosh; Shubhabrata Datta
      Pages: 297 - 306
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Subhamita Chakraborty, Partha P. Chattopadhyay, Swarup K. Ghosh, Shubhabrata Datta
      Artificial neural network model is developed for the prediction of phase transformation of steel from austenite, and thus construction of the continuous cooling transformation (CCT) diagram. The model for prediction of transformation temperatures from steel composition is developed using data from published CCT diagrams. The trained network sometimes fails to predict the sequence of the phase transformation, contradicting the fundamentals of metallurgy. To avoid such limitations of data driven models and to make the models truthful and reasonable from metallurgical standpoint, prior knowledge is incorporated using genetic algorithm, through modifying the weights and biases of a trained neural network. The conventionally backpropagated multi-layered perceptron is modified from error minimization as well as knowledge incorporation point of view through formulation of the problem in both single and multi-objective optimization domains. The predictions of six transformation temperatures by the new models are found to be significantly better than the conventionally trained model.
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      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.05.001
      Issue No: Vol. 58 (2017)
       
  • Minimizing total resource consumption and total tardiness penalty in a
           resource allocation supply chain scheduling and vehicle routing problem
    • Authors: Alborz Hassanzadeh; Morteza Rasti-Barzoki
      Pages: 307 - 323
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Alborz Hassanzadeh, Morteza Rasti-Barzoki
      The increase in the consumption of resources in the past decade has caused an increase in the interest of the international academic community in the challenges to reduce such rapid consumption of resources. Every year, many researchers propose different methods by which resource consumption can be reduced. Material, equipment, and process refinement are vivid examples of such efforts. While these innovations can be very helpful, in several cases, however, they can be very costly and greatly time-consuming. In addition, decision-makers tend to tackle the problem of resource consumption, while maintaining the proper level of service. Thus, in this paper, we propose a new bi-objective mathematical model by which we can reduce the consumption of resources and energy, as well as decrease the tardiness penalty in a supply chain scheduling and vehicle routing problem. The model demonstrates that finding the proper production (assembly) sequence, assignment of orders to vehicles and vehicle routing, will enable us to reduce resource consumption. A new Non-dominated Sorting Genetic Algorithm based on shaking and local search strategies of Variable Neighborhood Search algorithm is also developed to solve the proposed problem. Several criteria are introduced and defined to assess the performance of the proposed algorithm. Results demonstrate the out-performance of the proposed algorithm compared with the classic non-dominated sorting genetic algorithm II. We also propose a method that allows decision makers to make an informed decision to choose a proper sequence of jobs and routes that create a trade-off between resource consumption and the tardiness penalty.
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      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.05.010
      Issue No: Vol. 58 (2017)
       
  • Commentary on “Calculating fuzzy inverse matrix using fuzzy linear
           equation system”
    • Authors: Jagdeep Kaur; Amit Kumar
      Pages: 324 - 327
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Jagdeep Kaur, Amit Kumar
      Basaran [Calculating fuzzy inverse matrix using fuzzy linear equation system, Applied Soft Computing, 12 (2012), 1810–1813] proposed a method for finding the inverse of a fuzzy matrix by assuming all the elements of the fuzzy inverse matrix as non-negative fuzzy numbers, while some of the elements of fuzzy matrix inverse may also be negative fuzzy numbers. Keeping the same in mind, Mosleh and Otadi [A discussion on “Calculating fuzzy inverse matrix using fuzzy linear equation system”, Applied Soft Computing, 28 (2015), 511–513] assumed (i, j) element x ˜ ij = ( x ij , α ij , β ij ) of the fuzzy inverse matrix as a non-negative fuzzy number if the value of x ij obtained by Basaran's approach, is a non-negative real number and a negative fuzzy number if the value of x ij is negative real number. In this paper, it is shown that the fuzzy multiplicative inverse of a fuzzy matrix, obtained by considering this assumption, is also not an exact fuzzy multiplicative inverse. Furthermore, the required modifications, in Mosleh and Otadi's approach, to obtain the exact multiplicative inverse of a fuzzy matrix are suggested.

      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.04.026
      Issue No: Vol. 58 (2017)
       
  • A tribe competition-based genetic algorithm for feature selection in
           pattern classification
    • Authors: Benteng Ma; Yong Xia
      Pages: 328 - 338
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Benteng Ma, Yong Xia
      Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.
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      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.04.042
      Issue No: Vol. 58 (2017)
       
  • An adaptive consensus method for multi-attribute group decision making
           under uncertain linguistic environment
    • Authors: Jifang Pang; Jiye Liang; Peng Song
      Pages: 339 - 353
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Jifang Pang, Jiye Liang, Peng Song
      For a multi-attribute group decision making (MAGDM) problem, the so-called consensus reaching process is used to achieve an agreement among experts and finally make a common decision. Unfortunately, so far the consensus models for MAGDM haven’t been completely studied, especially for MAGDM under uncertain linguistic environment. The disadvantages of most existing consensus models could be summarized into 3 aspects. (1) In most existing consensus models, all the experts’ opinions are weighted equally important, and/or all the experts’ weights are treated statically. (2) Most of the interactive consensus methods are lack of effective feedback mechanism, while the automatic ones also have some defects, such as the lack of pertinence in adjustment process and the inability to reflect the subjective opinions of experts. (3) Also the comparison methods for uncertain linguistic variables therein are far from perfect, which require either complicated computing process or may cause non-distinguishable cases. In order to solve the above problems and obtain final decision results more efficiently, an interactive method with adaptive experts’ weights and explicit guidance rules for MAGDM under uncertain linguistic environment is developed. Our contributions can be summarized as follows. (1) Based on the definitions of closeness and consensus indices, a non-linear programming model is constructed to dynamically adjust the experts’ weights by maximizing the group consensus. (2) A targeted feedback mechanism including identification rules and recommendation rules is designed to guide the experts to modify their opinions more precisely and effectively. (3) A more appropriate method for comparing uncertain linguistic variables named dominance index is proposed, which can simplify the calculation process significantly. Finally, an illustrative example proves that the proposed consensus method is feasible and effective, and a detailed comparison and analysis highlights the advantages and characteristics of this method.
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      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.asoc.2017.04.039
      Issue No: Vol. 58 (2017)
       
  • Adaptive neuro fuzzy inference system for chart pattern matching in
           financial time series
    • Authors: Yuqing Wan; Yain-Whar Si
      Pages: 1 - 18
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Yuqing Wan, Yain-Whar Si
      In technical analysis, the appearance of chart patterns in financial time series is considered as one of the crucial signals in predicting future price trend. In recent years, various classification methods have been proposed by researchers to locate and identify potential chart patterns from input time series. This paper presents a novel application of adaptive neuro fuzzy inference system (ANFIS) for chart pattern matching in financial time series. The construction of ANFIS for chart patterns is described in the paper. In addition, we propose a method to determine the thresholds to implement chart patterns matching with the trained ANFIS model. The effectiveness and efficiency of the ANFIS model are compared with six pattern matching approaches on both synthetic datasets and real datasets. Experimental results reveal that the ANFIS model is effective in classifying different chart patterns when compared with other pattern matching approaches.
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      PubDate: 2017-04-12T08:11:17Z
      DOI: 10.1016/j.asoc.2017.03.023
      Issue No: Vol. 57 (2017)
       
  • A cooperative negotiation embedded NSGA-II for solving an integrated
           product family and supply chain design problem with remanufacturing
           consideration
    • Authors: Zhiqiao Wu; C.K. Kwong; Ridvan Aydin; Jiafu Tang
      Pages: 19 - 34
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Zhiqiao Wu, C.K. Kwong, Ridvan Aydin, Jiafu Tang
      Product family design is a popular approach adopted by manufacturers to increase their product varieties in order to satisfy the needs of various markets. In recent years, because of increasing environmental concerns in societies and strict regulations of environmental protection, quite a number of manufacturers adopted remanufacturing strategy in their product development in response to the challenges. Remanufacturing of used products unavoidably involves a closed-loop supply chain system. To achieve the best outcomes, the supply chain design should be considered in product family design process. In this research, a multi-objective optimization model of integrated product family and closed loop supply chain design is formulated based on a cooperative game model for minimizing manufacturer’s total cost and maximize suppliers’ total payoffs. Since the optimization problem could be a large- scale one and involves mixed continuous-discrete variables, a new version of nondominated sorting genetic algorithm-II (NSGA-II), namely cooperative negotiation embedded NSGA-II (NSGA-CO), is proposed to solve the optimization model. Simulation tests are conducted to validate the effectiveness of the proposed NSGA-CO. The test results indicate that the proposed NSGA-CO outperforms NSGA-II in solving various scale of multi-objective optimization problems in terms of convergence. With the formulated optimization model and the proposed NSGA-CO, a case study of integrated product family and supply chain design is conducted to investigate the effects of environmental penalty, quantity of demand and marginal cost of remanufacturing on used product return rate, manufacturers’ and suppliers’ profits and joint payoff.
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      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.021
      Issue No: Vol. 57 (2017)
       
  • Mobile robot path planning with surrounding point set and path improvement
    • Authors: Jihee Han; Yoonho Seo
      Pages: 35 - 47
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Jihee Han, Yoonho Seo
      The objective of the path planning problem for a mobile robot is to generate a collision-free path from a starting position to a target position with respect to a certain fitness function, such as distance. Although, over the last few decades, path planning has been studied using a number of methodologies, the complicated and dynamic environment increases the complexity of the problem and makes it difficult to find an optimal path in reasonable time. Another issue is the existence of uncertainty in previous approaches. In this paper, we propose a new methodology to solve the path planning problem in two steps. First, the surrounding point set (SPS) is determined where the obstacles are circumscribed by these points. After the initial feasible path is generated based on the SPS, we apply a path improvement algorithm depending upon the former and latter points (PI_FLP), in which each point in the path is repositioned according to two points on either side. Through the SPS, we are able to identify the necessary points for solving path planning problems. PI_FLP can reduce the overall distance of the path, as well as achieve path smoothness. The SPS and PI_FLP algorithms were tested on several maps with obstacles and then compared with other path planning methods As a result, collision-free paths were efficiently and consistently generated, even for maps with narrow geometry and high complexity.
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      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.035
      Issue No: Vol. 57 (2017)
       
  • A framework for objective image quality measures based on intuitionistic
           fuzzy sets
    • Authors: M. Hassaballah; A. Ghareeb
      Pages: 48 - 59
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): M. Hassaballah, A. Ghareeb
      Measuring the distance or similarity objectively between images is an essential and a challenging problem in various image processing and pattern recognition applications. As it is very difficult to find a certain measure that can be successfully applied to all kinds of images comparisons-related problems in the same time, it is appropriate to look for new approaches for measuring the similarity. Several similarity measures tested on numerical cases are developed in the literature based on intuitionistic fuzzy sets (IFSs) without evaluation on real data. This paper introduces a framework for using the similarity measures on IFSs in image processing field, specifically for image comparison. First, some existing similarity measures are discussed and highlighted their properties. Then, modeling digital images using IFSs is explained. Moreover, the paper introduces an intuitionistic fuzzy based image quality index measure. Second, for improving the perceived visual quality of these IFS-based similarity measures, construction of neighborhood-based similarity is proposed, which takes into consideration homogeneity of images. Finally, the proposed framework is verified on real world natural images under various types of image distortions. Experimental results confirm the effectiveness of the proposed framework in measuring the similarity between images.
      Graphical abstract image Highlights

      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.046
      Issue No: Vol. 57 (2017)
       
  • An adaptive differential evolution algorithm with an aging leader and
           challengers mechanism
    • Authors: C.M. Fu; C. Jiang; G.S. Chen; Q.M. Liu
      Pages: 60 - 73
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): C.M. Fu, C. Jiang, G.S. Chen, Q.M. Liu
      An adaptive differential evolution algorithm with an aging leader and challengers mechanism, called ADE-ALC, is proposed to solve optimization problems. In ADE-ALC algorithm, the aging mechanism is introduced into the framework of differential evolution to maintain diversity of the population. The key control parameters are adaptively updated based on given probability distributions which could learn from their successful experiences to generate the promising parameters at the next generation. One of the two local search operators is randomly selected to generate challengers which are beneficial for increasing the diversity of population. Finally, the effectiveness of the ADE-ALC algorithm is verified by the numerical results of twenty-five benchmark test functions.
      Graphical abstract image

      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.032
      Issue No: Vol. 57 (2017)
       
  • Sustainable market valuation of buildings by the single-valued
           neutrosophic MAMVA method
    • Authors: Edmundas Kazimieras Zavadskas; Romualdas Bausys; Arturas Kaklauskas; Ieva Ubarte; Agne Kuzminske; Neringa Gudiene
      Pages: 74 - 87
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Edmundas Kazimieras Zavadskas, Romualdas Bausys, Arturas Kaklauskas, Ieva Ubarte, Agne Kuzminske, Neringa Gudiene
      Traditionally, the real estate asset assessment is performed by experienced valuators, who take into account its economic, social, physical and locational aspects. Nowadays, the construction industry is becoming more and more influenced by the sustainability requirements. Therefore, the inclusion of the sustainability evaluation into real estate asset valuation is of utmost importance. The Neutrosophic Multi-Attribute Market Value Assessment (MAMVA) method developed by the authors of this article handles market value calculations by solving multiple criteria assessment problems, and the initial information vagueness is modelled explicitly. The supplementary novelty of the present paper is the inclusion of the sustainability aspects into the real estate market valuation. The sustainable market valuation of Croydon University Hospital (Emergency Department) is performed as the case study to present numerical capabilities of the proposed approach. Our research findings suggest that neutrosophic MAMVA is a rational approach for calculations of property market valuation and might be suitable for application worldwide.
      Graphical abstract image

      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.040
      Issue No: Vol. 57 (2017)
       
  • Selecting skyline stars over uncertain databases: Semantics and refining
           methods in the evidence theory setting
    • Authors: Sayda Elmi; Mohamed Anis Bach Tobji; Allel Hadjali; Boutheina Ben Yaghlane
      Pages: 88 - 101
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Sayda Elmi, Mohamed Anis Bach Tobji, Allel Hadjali, Boutheina Ben Yaghlane
      In recent years, a great attention has been paid to skyline computation over uncertain data. In this paper, we study how to conduct advanced skyline analysis over uncertain databases where uncertainty is modeled thanks to the evidence theory (a.k.a., belief functions theory). We particularly tackle an important issue, namely the skyline stars (denoted by SKY2) over the evidential data. This kind of skyline aims at retrieving the best evidential skyline objects (or the stars). Efficient algorithms have been developed to compute the SKY2. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches that considerably refine the huge skyline. In addition, the conducted experiments have shown that our algorithms significantly outperform the basic skyline algorithms in terms of CPU and memory costs.
      Graphical abstract image Highlights

      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.025
      Issue No: Vol. 57 (2017)
       
  • Content-based image retrieval based on multiple extended fuzzy-rough
           framework
    • Authors: Sk Mazharul Islam; Minakshi Banerjee; Siddhartha Bhattacharyya; Susanta Chakraborty
      Pages: 102 - 117
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Sk Mazharul Islam, Minakshi Banerjee, Siddhartha Bhattacharyya, Susanta Chakraborty
      This paper presents a content-based image retrieval (CBIR) system with applications in one general purpose and two face image databases using two MPEG-7 image descriptors. The proposed method uses several sophisticated fuzzy-rough feature selection methods and combines the results of these methods to obtain a prominent feature subset for image representation for a particular query. Next, fuzzy-rough upper approximation of the target set (relevant list of images) with respect to the entire database that is represented by the prominent feature subset, is computed for retrieval and ranking. The information table on which every feature selection method works is small in size. Main reasons of performance boost of the proposed method are twofold. One is efficient feature subsets selection. The other reason is the fuzzy-indiscernibility relation based fuzzy-rough framework for computing upper-approximation which supports the approximate equality or similarity sense of CBIR. Fuzzy-rough upper approximation possibly adds more similar images in the relevant list from boundary region to expand the relevant list. The effectiveness of the proposed method is supported by the comparative results obtained from several single dimensionality reduction method, several clustering based retrieval techniques and also tested for face image retrieval.
      Graphical abstract image Highlights

      PubDate: 2017-04-26T15:33:59Z
      DOI: 10.1016/j.asoc.2017.03.036
      Issue No: Vol. 57 (2017)
       
  • Artificial immune systems applied to fault detection and isolation: A
           brief review of immune response-based approaches and a case study
    • Authors: Guilherme Costa Silva; Walmir Matos Caminhas; Reinaldo Martinez Palhares
      Pages: 118 - 131
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Guilherme Costa Silva, Walmir Matos Caminhas, Reinaldo Martinez Palhares
      This paper aims to document the application of a new generation of artificial immune systems (AIS) in fault detection and isolation problems. These kind of algorithms are able to explore normal and anomalous behavior evidences, however, they may often require a more explicit prior knowledge provided by experts, usually difficult to obtain in some practical cases. Thus, many immune inspired approaches applied to fault detection and isolation (FDI) in the literature are based on negative selection algorithms. Considering these points, this work presents a review on three AIS approaches. Once reviewed and contextualized, the evaluated techniques are properly adjusted considering their main parameters and ways of processing data, and then, applied to a case study of fault detection and isolation in order to provide a performance analysis of these techniques, according to their applicability to these problems.
      Graphical abstract image Highlights

      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.031
      Issue No: Vol. 57 (2017)
       
  • Fuzzy inference suitability to determine the utilitarian quality of B2C
           websites
    • Authors: Adrian Castro-Lopez; Javier Puente; Rodolfo Vazquez-Casielles
      Pages: 132 - 143
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Adrian Castro-Lopez, Javier Puente, Rodolfo Vazquez-Casielles
      The present paper proposes a new and intuitive model to evaluate the utilitarian e-service quality on textile and fashion B2C websites in Spain. The relevant variables to include into this model have been validated through an in-depth literature review and a reliability study based on an empirical investigation. Among the several methodologies used to develop the model, the paper proves that fuzzy inference systems (FIS) have numerous advantages. The following are particularly relevant: the ease to manage non-linear behaviours and the intuitive way of incorporating knowledge to evaluate B2C websites. This knowledge can stem both: from the expert know-how or from the evaluation of consumer satisfaction on these websites. The knowledge is made explicit through if-then rules which work with linguistic-type concepts −agreed upon by experts, closer to the human reasoning mode. Finally, the usefulness of the proposed model and the defined FIS suitability to evaluate the model are shown.
      Graphical abstract image

      PubDate: 2017-04-26T15:33:59Z
      DOI: 10.1016/j.asoc.2017.03.039
      Issue No: Vol. 57 (2017)
       
  • A local search enhanced differential evolutionary algorithm for sparse
           recovery
    • Authors: Qiuzhen Lin; Bishan Hu; Ya Tang; Leo Yu Zhang; Jianyong Chen; Xiaomin Wang; Zhong Ming
      Pages: 144 - 163
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Qiuzhen Lin, Bishan Hu, Ya Tang, Leo Yu Zhang, Jianyong Chen, Xiaomin Wang, Zhong Ming
      Signal recovery problem in compressed sensing can be mathematically modeled as a ℓ0 regularized problem, which aims at searching a sparse solution. When tackling this problem, traditional mathematical approaches suffer from a limited convergence ability, especially under the noisy condition. To better solve this problem, in this paper, a novel differential evolutionary (DE) algorithm is designed to combine with a local search approach. First, an adaptive control strategy for DE is extended to recover sparse signals with noise in this paper, which is found to have a promising recovery performance. Second, in order to further enhance the convergence speed, a local search approach, i.e., a shrinkage-thresholding method (STM), is embedded into the evolutionary process of DE. Therefore, the advantages of local search capability provided by STM and global search ability of DE can be effectively combined, and resultantly a novel local search enhanced adaptive DE (named LSE-ADE) algorithm is proposed. Experimental results validate that LSE-ADE performs better than the eight classic sparse recovery algorithms and one recently proposed evolutionary algorithm, when recovering sparse signal under the noisy condition.
      Graphical abstract image

      PubDate: 2017-04-26T15:33:59Z
      DOI: 10.1016/j.asoc.2017.03.034
      Issue No: Vol. 57 (2017)
       
  • Evolutionary strategies as applied to shear strain effects in reinforced
           concrete beams
    • Authors: Raquel Martínez España; A.M. Hernández-Díaz; José M. Cecilia; Manuel D. García-Román
      Pages: 164 - 176
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Raquel Martínez España, A.M. Hernández-Díaz, José M. Cecilia, Manuel D. García-Román
      The reinforced concrete beams is a structural member that is widely used in all types of building and civil constructions. These beams are subjected to different external loads that, above a critical value, may cause the collapse of the whole structure, having devastating consequences for civilians. Therefore, the a priori simulation of the internal forces developed within a reinforced concrete beam, when it is subjected to external loads, is mandatory to figure out its progressive structural response, to provide integrated risk assessment for a wide range of constructions such as buildings, bridges, etc. In this paper, we provide a simulation framework to estimate the behavior of reinforced concrete beams when they are subjected to external loads. Of particular interest to us is the simulation of the particularly damaging internal force, called Shear force. Several techniques are under study such as regression analysis, Little Genetic Algorithm (LGA) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), along with different objective functions (lineal, polynomial and rational functions) to provide a solution that satisfies both the physical and computational constraints of the targeted problem. These techniques are empirically optimized by using different parameters and genetic operators such as elitism, penalization for unfeasible individuals, crossing by one point or by linear combination of two individuals, mutation by gen or by individual. Numerical results reveal that CMA-ES algorithm together with a proper objective function, elitism and penalization allows predicting, under a relative error less than 5% (compared to experimental data taken from a tested beam), the shear response of a reinforced concrete beam in the stages near to the structural collapse.
      Graphical abstract image Highlights An inverse analysis of the degradation of the bond between the concrete and the reinforcement, assuming the so-called tension stiffening area in the concrete as a function of the shear strain is performed. This problem is studied and analyzed using a preliminary least squares method and two evolutionary strategies in order to provide a solution that satisfies both the physical and computational constraints of the problem.

      PubDate: 2017-04-19T15:24:20Z
      DOI: 10.1016/j.asoc.2017.03.037
      Issue No: Vol. 57 (2017)
       
  • A multi-resolution wavelet neural network approach for fouling resistance
           forecasting of a plate heat exchanger
    • Authors: Xiaoqiang Wen; Qinglong Miao; Jianguo Wang; Zhoulei Ju
      Pages: 177 - 196
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Xiaoqiang Wen, Qinglong Miao, Jianguo Wang, Zhoulei Ju
      A new framework regarding wavelet neural network, termed a multi-resolution wavelet neural network (MRWNN), is composed based on the theory of multi-resolution wavelet analysis and orthogonal multi-scale spaces. The hidden layer of the network is divided into two parts, neurons with the Meyer scaling activation function and the Meyer wavelet activation function which is orthogonal to the scaling function. Neurons with the scaling function approximate the contour of the aimed function for its lentitude, and neurons with the wavelet function approximate the details of the aimed function for its sensitive trend. Hidden neurons are mapped to different resolution spaces by redefining the network frame depending on the multi-resolution wavelet analysis theory. By incorporating the Gradient Descent Algorithm, the network can be optimized with less interaction within hidden neurons, and thus, it will acquire a further error convergence state when all the correspondent parameters are adjusted in different resolution spaces. When applied to fouling forecasting of a plate heat exchanger, the MRWNN achieved better performance than other neural networks (NNs) when applied to simulations, proving that the MRWNN is effective in nonlinear function approximations.
      Graphical abstract image

      PubDate: 2017-04-26T15:33:59Z
      DOI: 10.1016/j.asoc.2017.03.043
      Issue No: Vol. 57 (2017)
       
  • Robust nonlinear channel equalization using WNN trained by symbiotic
           organism search algorithm
    • Authors: Satyasai Jagannath Nanda; Nidhi Jonwal
      Pages: 197 - 209
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Satyasai Jagannath Nanda, Nidhi Jonwal
      In the present world of ‘Big Data,’ the communication channels are always remaining busy and overloaded to transfer quintillion bytes of information. To design an effective equalizer to prevent the inter-symbol interference in such scenario is a challenging task. In this paper, we develop equalizers based on a nonlinear neural structure (wavelet neural network (WNN)) and train it's weighted by a recently developed meta-heuristic (symbiotic organisms search algorithm). The performance of the proposed equalizer is compared with WNN trained by cat swarm optimization (CSO) and clonal selection algorithm (CLONAL), particle swarm optimization (PSO) and least mean square algorithm (LMS). The performance is also compared with other equalizers with structure based on functional link artificial neural network (trigonometric FLANN), radial basis function network (RBF) and finite impulse response filter (FIR). The superior performance is demonstrated on equalization of two non-linear three taps channels and a linear twenty-three taps telephonic channel. It is observed that the performance of the gradient algorithm based equalizers fails in the presence of burst error. The robustness in the performance of the proposed equalizers to handle the burst error conditions is also demonstrated.
      Graphical abstract image Highlights The fundamental process of symbiosis in an ecosystem.

      PubDate: 2017-04-26T15:33:59Z
      DOI: 10.1016/j.asoc.2017.03.029
      Issue No: Vol. 57 (2017)
       
  • A heterogeneous fuzzy collaborative intelligence approach for forecasting
           the product yield
    • Authors: Toly Chen
      Pages: 210 - 224
      Abstract: Publication date: August 2017
      Source:Applied Soft Computing, Volume 57
      Author(s): Toly Chen
      For manufacturers, forecasting the future yield of a product is a critical task. However, a yield learning process involves considerable uncertainty, rendering the task difficult. Although a few fuzzy collaborative intelligence (FCI) methods have been proposed in recent years, they are not problem-free. Hence, to overcome the challenges associated with these methods and to improve the accuracy of future yield forecasts, a heterogeneous FCI approach is proposed in this study. In this method, an expert applies mathematical-programming-based or artificial-neural-network-based methods (i.e., heterogeneous methods) to model an uncertain yield learning process. Subsequently, fuzzy intersection narrows the possible range of the future yield, and finally, an artificial neural network derives a crisp (representative) value. The effectiveness of the proposed heterogeneous FCI approach was successfully demonstrated by considering data obtained from a factory manufacturing dynamic random access memory devices. The approach achieved an average increase of 21% in the forecasting accuracy compared with existing approaches.
      Graphical abstract image

      PubDate: 2017-04-26T15:33:59Z
      DOI: 10.1016/j.asoc.2017.04.009
      Issue No: Vol. 57 (2017)
       
  • An efficient multi-objective artificial raindrop algorithm and its
           application to dynamic optimization problems in chemical processes
    • Authors: Qiaoyong Jiang; Lei Wang; Jiatang Cheng; Xiaoshu Zhu; Wei Li; Yanyan Lin; Guolin Yu; Xinhong Hei; Jinwei Zhao; Xiaofeng Lu
      Pages: 111 - 128
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Qiaoyong Jiang, Lei Wang, Yanyan Lin, Xinhong Hei, Guolin Yu, Xiaofeng Lu
      Recently, a new meta-heuristic approach, known as artificial raindrop algorithm (ARA), was proposed. This approach is inspired by the natural rainfall phenomenon, and has been observed to be a powerful tool in solving single-objective optimization problems. In this paper, a multi-objective variant of ARA (MOARA) is developed, which primarily combines the search mechanism of ARA and the non-dominated sorting technique, in an attempt to demonstrate the potential of ARA in tackling multi-objective optimization problems (MOPs). To improve the exploratory ability, the center point sampling strategy (CPSS) together with simulated binary crossover (SBX) is integrated into MOARA. The primary role of SBX is to accelerate the filling of the Pareto front (PF) by recombining diverse solutions, whereas CPSS serves as the domain knowledge of the MOP for guiding other points toward the target PF. For performance evaluation and comparison purposes, the proposed approach has been applied to two sets of benchmark MOPs, and compared with eight state-of-the-art multi-objective evolutionary algorithms based on the non-dominated sorting. The experimental results have indicated its improved efficiency over the other compared approaches. Furthermore, the contributions of SBX and CPSS to the entire algorithm have been experimentally studied. Finally, the proposed technique is applied to dynamic optimization problems in chemical processes, and promising results show the method's potential in real-world applications.
      Graphical abstract image Highlights An efficient multi-objective artificial raindrop algorithm.

      PubDate: 2017-05-18T08:23:59Z
      DOI: 10.1016/j.knosys.2017.01.020
      Issue No: Vol. 121 (2017)
       
  • FPGA based hardware implementation of Bat Algorithm
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Mohamed Sadok Ben Ameur, Anis Sakly
      Several meta-heuristics algorithms are used mainly for research of global optimal solutions for real and non-convex problems. Some of them are the Genetic Algorithms (GA), Cuckoo Search algorithms (CS), Particle Swarm Optimization (PSO). Some algorithms have achieved satisfactory results but not all of them. Therefore, new algorithms give better optimization to solve many problems having continuous search space like Bat Algorithm (BA). That’s why we proposed a new hardware implementation on Field Programmable Gate Array (FPGA) of bat algorithm, it is a new proposed meta-heuristic for global optimization. The work presented in this article is designed to use new digital dedicated hardware solutions such as FPGAs that are available to generate a better implementation of bat algorithm. This circuit is well adapted to many applications because its material structure is molded with the requirements of calculations. Moreover the inherent parallelism of these new hardware solutions and their large computing capabilities makes the computing time negligible despite the complexity of these algorithms.
      Graphical abstract image

      PubDate: 2017-05-23T02:06:38Z
       
  • Graphical model based continuous estimation of distribution algorithm
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Latifeh PourMohammadBagher, Mohammad Mehdi Ebadzadeh, Reza Safabakhsh
      In this paper, a new estimation of distribution algorithm is introduced. The goal is to propose a method that avoids complex approximations of learning a probabilistic graphical model and considers multivariate dependencies between continuous random variables. A parallel model of some subgraphs with a smaller number of variables is learned as the probabilistic graphical model. In each generation, the joint probability distribution of the selected solutions is estimated using a Gaussian Mixture model. Then, learning the graphical model of dependencies among random variables and sampling are done separately for each Gaussian component. In the learning step, using the selected solutions of each Gaussian mixture component, the structure of a Markov network is learned. This network is decomposed to maximal cliques and a clique graph. Then, complete Bayesian network structures are learned for these subgraphs using an optimization algorithm. The proposed optimization problem is a 0–1 constrained quadratic programming which finds the best permutation of variables. Then, sampling is done from each Bayesian network of each Gaussian component. The introduced method is compared with the other network-based estimation of distribution algorithms for optimization of continuous numerical functions.
      Graphical abstract image

      PubDate: 2017-05-23T02:06:38Z
       
  • Orthogonal PSO algorithm for economic dispatch of thermal generating units
           under various power constraints in smart power grid
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Loau Tawfak Al Bahrani, Jagdish Chandra Patra
      Particle swarm optimization (PSO) algorithm has been successfully applied to solve various optimization problems in science and engineering. One such popular one is called global PSO (GPSO) algorithm. One of major drawback of GPSO algorithm is the phenomenon of “zigzagging”, that leads to premature convergence by falling into local minima. In addition, the performance of GPSO algorithm deteriorates for high-dimensional problems, especially in presence of nonlinear constraints. In this paper we propose a novel algorithm called, orthogonal PSO (OPSO) that alleviates the shortcomings of the GPSO algorithm. In OPSO algorithm, the m particles of the swarm are divided into two groups: active group and passive group. The d particles of the active group undergo an orthogonal diagonalization process and are updated in such way that their position vectors become orthogonally diagonalized. In the OPSO algorithm, the particles are updated using only one guide, thus avoiding the conflict between the two guides that occurs in the GPSO algorithm. We applied the OPSO algorithm for solving economic dispatch (ED) problem by taking three power systems under several power constraints imposed by thermal generating units (TGUs) and smart power grid (SPG), for example, ramp rate limits, and prohibited operating zones. In addition, the OPSO algorithm is also applied for ten selected shifted and rotated CEC 2015 benchmark functions. With extensive simulation studies, we have shown superior performance of OPSO algorithm over GPSO algorithm and several existing evolutional computation techniques in terms of several performance measures, e.g., minimum cost, convergence rate, consistency, and stability. In addition, using unpaired t-Test, we have shown the statistical significance of the OPSO algorithm against several contending algorithms including top-ranked CEC 2015 algorithms.
      Graphical abstract image

      PubDate: 2017-05-23T02:06:38Z
       
  • ACO-inspired ICN routing mechanism with mobility support
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Jianhui Lv, Xingwei Wang, Min Huang
      Nowadays, Information-Centric Networking (ICN) has been recognized as a promising paradigm in which users retrieve named content with information-centric communication mode rather than finding IP address with host-centric communication mode. Although ICN routing has attracted much attention from researchers, the current proposals cannot effectively and intelligently solve the mobility problem in a self-adaptive and self-organizing manner. In this paper, we introduce Ant Colony Optimization (ACO) into ICN and propose a novel ACO-inspired ICN Routing mechanism with Mobility support (AIRM) to retrieve the content no matter where it moves. At first, a continuous pheromone updating strategy inspired by alcohol volatilization model is devised to conduct the forwarding of interest ant. Secondly, we determine which outgoing interfaces can be used to forward interest ant, propose a computation scheme to obtain the forwarding probability, and select an outgoing interface to forward interest ant by roulette model. Thirdly, the detailed design on AIRM is presented to address mobility while retrieve the closest content copy. Finally, we evaluate the proposed AIRM, and the simulation results show that AIRM not only solves the mobility problem effectively but also has better performance than existent schemes in terms of routing success rate, routing hop count, load balance degree and execution time.
      Graphical abstract image Highlights

      PubDate: 2017-05-23T02:06:38Z
       
  • Estimating the shear stress distribution in circular channels based on the
           randomized neural network technique
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Zohreh Sheikh Khozani, Hossein Bonakdari, Amir Hossein Zaji
      Predicting the shear stress distribution in channels is crucial in hydraulic engineering problems. Since the equations presented by other researchers for estimating the shear stress distribution in circular channels are complicated, this study focuses on applying Randomized Neural Networks (RNN) to present easier equations for computing the shear stress distribution. The specific aim of this work is to obtain accurate shear stress distribution estimation, something proven difficult through experimental and analytical methods 176 data for four circular channel flow depths serve as the entire dataset, and half are used as the testing dataset. Sensitivity analysis is applied and 15 RNN models with different input combinations are investigated. The model with Re and y/P as input variables produces the most appropriate results in predicting shear stress distribution. The best RNN model (model 10) is also compared with an equation based on the Shannon entropy. The study provides evidence that RNN Model 10 (with average RMSE of 0.0544 and MAE of 0.0463) is capable of modelling the shear stress distribution in circular channels and is more accurate than the Shannon entropy-based equation (with average RMSE of 0.1050 and MAE of 0.0840).
      Graphical abstract image

      PubDate: 2017-05-23T02:06:38Z
       
  • Flexible job shop scheduling under condition-based maintenance: Improved
           version of imperialist competitive algorithm
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): M. Zandieh, A.R. Khatami, Seyed Habib A. Rahmati
      Maintenance activities have been ignored in many studies on scheduling problems where all machines are assumed to be available without interruption in the planning horizon. However, in realistic situations, they might be unavailable due to preventive maintenance, basic maintenance or unforeseen breakdowns. In this paper, we simulate a condition-based maintenance (CBM) for flexible job shop scheduling problem (FJSP) and consider the combination of Sigmoid function and Gaussian distribution to improve the CBM simulation. This study proposes an improved imperialist competitive algorithm (ICA) for the FJSP scheduling problem with the objective of the makespan minimization. The performance of the proposed algorithm is enhanced with a hybridization of ICA with simulated annealing (SA), after diagnosing standard ICA disadvantages and shortcomings. This ICA also includes a simulation part to handle CBM requirements. Various parameters of the novel ICA are reviewed to calibrate the algorithm with the help of the Taguchi experimental design. Experimental results show the high performance of the novel ICA in comparison with the standard ICA. The obtained results demonstrate that the novel ICA is an effective algorithm for FJSP under CBM. Finally, the performance of ICA is evaluated compared to other popular algorithms.
      Graphical abstract image

      PubDate: 2017-05-23T02:06:38Z
       
  • A new MPPT controller based on the Ant colony optimization algorithm for
           Photovoltaic systems under partial shading conditions
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Sabrina Titri, Cherif Larbes, Kamal Youcef Toumi, Karima Benatchba
      The Maximum Power Point Tracking controller (MPPT) is a key element in Photovoltaic systems (PV). It is used to maintain the PV operating point at its maximum under different temperatures and sunlight irradiations. The goal of a MPPT controller is to satisfy the following performances criteria: accuracy, precision, speed, robustness and handling the partial shading problem when climatic changes variations occur. To achieve this goal, several techniques have been proposed ranging from conventional methods to artificial intelligence and bio-inspired methods. Each technique has its own advantage and disadvantage. In this context, we propose in this paper, a new Bio- inspired MPPT controller based on the Ant colony Optimization algorithm with a New Pheromone Updating strategy (ACO_NPU MPPT) that saves the computation time and performs an excellent tracking capability with high accuracy, zero oscillations and high robustness. First, the different steps of the design of the proposed ACO_NPU MPPT controller are developed. Then, several tests are performed under standard conditions for the selection of the appropriate ACO_NPU parameters (number of ants, coefficients of evaporation, archive size, etc.). To evaluate the performances of the obtained ACO_NPU MPPT, in terms of its tracking speed, accuracy, stability and robustness, tests are carried out under slow and rapid variations of weather conditions (Irradiance and Temperature) and under different partial shading patterns. Moreover, to demonstrate the superiority and robustness of the proposed ACO_NPU_MPPT controller, the obtained results are analyzed and compared with others obtained from the Conventional Methods (P&O_MPPT) and the Soft Computing Methods with Artificial intelligence (ANN_MPPT, FLC_MPPT, ANFIS_MPPT, FL_GA_MPPT) and with the Bio Inspired methods (PSO) and (ACO) from the literature. The obtained results show that the proposed ACO_NPU MPPT controller gives the best performances under variables atmospheric conditions. In addition, it can easily track the global maximum power point (GMPP) under partial shading conditions.

      PubDate: 2017-05-23T02:06:38Z
       
  • An enhanced artificial bee colony algorithm with adaptive differential
           operators
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Zhengping Liang, Kaifeng Hu, Quanxiang Zhu, Zexuan Zhu
      Artificial bee colony algorithm (ABC) has been shown to be very effective to solve global optimization problems (GOPs). However, ABC performs well in exploration but relatively poorly in exploitation resulting in a slow convergence when it is used to handle complex GOPs. Differential evolution (DE) benefits from its differential operators, namely mutation operator and crossover operator, which could perturb multiple variables simultaneously and has shown a fast convergence speed. In order to improve ABC’s exploitation ability and accelerate its convergence, in this paper, we propose an enhanced ABC algorithm named ABCADE, which remedy the limitation of ABC by exploiting the advantage of differential operators. Particularly, in ABCADE, the employed bees employ differential operators to produce candidate solutions with an increasing probability, and the two important parameters (scale factor F and crossover rate CR) of differential operators are adaptively adjusted through Gaussian distribution. Moreover, to significantly differentiate the good solutions and bad solutions in a population, and put more effort in the exploitation around the good solutions, we design a new selection probability method for onlooker bees. To verify the performance of ABCADE, we compare ABCADE with other representative state-of-the-art ABC and DE algorithms, the comparison results on a set of 22 benchmark functions with various dimension sizes demonstrate that ABCADE obtains superior or comparable performance to other algorithms.
      Graphical abstract image

      PubDate: 2017-05-23T02:06:38Z
       
  • Flocking based evolutionary computation strategy for measuring centrality
           of online social networks
    • Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Dhinesh Babu L.D, Ebin Deni Raj
      Centrality in social network is one of the major research topics in social network analysis. Even though there are more than half a dozen methods to find centrality of a node, each of these methods has some drawbacks in one aspect or the other. This paper analyses different centrality calculation methods and proposes a new swarm based method named Flocking Based Centrality for Social network (FBCS). This new computation technique makes use of parameters that are more realistic and practical in online social networks. The interactions between nodes play a significant role in determining the centrality of node. The new method has been calculated both empirically as well as experimentally. The new method is tested, verified and validated for different sets of random networks and benchmark datasets. The method has been correlated with other state of the art centrality measures. The new centrality measure is found to be realistic and suits well with online social networks. The proposed method can be used in applications such as finding the most prestigious node and for discovering the node which can influence maximum number of users in an online social network. FBCS centrality has higher Kendall’s tau correlation when compared with other state of the art centrality methods. The robustness of the FBCS centrality is found to be better than other centrality measures.
      Graphical abstract image

      PubDate: 2017-05-23T02:06:38Z
       
  • A multi-phase oscillated variable neighbourhood search algorithm for a
           real-world open vehicle routing problem
    • Authors: Bekir
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Aişe Zülal Şevkli, Bekir Güler
      The aim of this study is to solve the newspaper delivery optimization problem for a media delivery company in Turkey by reducing the total cost of carriers. The problem is modelled as an open vehicle routing problem (OVRP), which is a variant of the vehicle routing problem. A variable neighbourhood search-based algorithm is proposed to solve a real-world OVRP. The proposed algorithm is tested with varieties of small and large-scale benchmark suites and a very large-scale real-world problem instance. The results of the proposed algorithm provide either the best known solution or a competitive solution for each of the benchmark instances. The algorithm also improves the real-world company’s solutions by more than 10%.
      Graphical abstract image

      PubDate: 2017-05-13T08:04:53Z
       
 
 
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