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  Subjects -> COMPUTER SCIENCE (Total: 1974 journals)
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    - COMPUTER SCIENCE (1148 journals)
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COMPUTER SCIENCE (1148 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: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 9)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 11)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 25)
Advanced Science Letters     Full-text available via subscription   (Followers: 6)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 13)
Advances in Computing     Open Access   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 54)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 24)
Advances in Human-Computer Interaction     Open Access   (Followers: 19)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access  
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 6)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 7)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 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: 14)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 1)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 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 and Museum Informatics     Hybrid Journal   (Followers: 121)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 5)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access  
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 3)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 9)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Basin Research     Hybrid Journal   (Followers: 3)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Bioinformatics     Hybrid Journal   (Followers: 259)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 32)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 123)
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: 13)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
Cell Communication and Signaling     Open Access   (Followers: 1)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
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: 14)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Communication Methods and Measures     Hybrid Journal   (Followers: 11)
Communication Theory     Hybrid Journal   (Followers: 19)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 51)
Communications of the Association for Information Systems     Open Access   (Followers: 18)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access  
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 1)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 8)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 13)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 13)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 28)
Computer     Full-text available via subscription   (Followers: 81)
Computer Aided Surgery     Hybrid Journal   (Followers: 3)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 7)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 10)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 1)
Computer Music Journal     Hybrid Journal   (Followers: 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: 11)
Computer Science Education     Hybrid Journal   (Followers: 12)
Computer Science Journal     Open Access   (Followers: 20)
Computer Science Master Research     Open Access   (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  [3042 journals]
  • Novel fuzzy linguistic based mathematical model to assess risk of invasive
           alien plant species
    • Authors: H.O.W. Peiris; S. Chakraverty; S.S.N. Perera; S.M.W. Ranwala
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): H.O.W. Peiris, S. Chakraverty, S.S.N. Perera, S.M.W. Ranwala
      The purpose of this study is to develop a mathematical model for risk assessment that evaluates the invasion risk of invasive alien plant species based on their biological attributes. Data for most of the attributes were qualitative and in the form of linguistic terms. In order to handle the numerical and linguistic variables, we proposed three models in the fuzzy environment. In the first model, all the selected attributes were considered as equally important to invasiveness and in the second and third models, these are considered to be unequally important. The important weights for the biological attributes in these models were gathered from the group of experts in plant sciences. Proposed Model III incorporates more sophisticated linguistic tool than Model II. Model II gives better predictions in comparison to the first and third models and it is found to be better tracking system for identifying potential invaders as in the case of conventional risk assessment method.
      Graphical abstract image

      PubDate: 2017-06-21T14:33:54Z
      DOI: 10.1016/j.asoc.2017.06.006
      Issue No: Vol. 59 (2017)
       
  • A method of defuzzification based on the approach of areas' ratio
    • Authors: Maxim V. Bobyr; Natalya A. Milostnaya; Sergey A. Kulabuhov
      Pages: 19 - 32
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Maxim V. Bobyr, Natalya A. Milostnaya, Sergey A. Kulabuhov
      The new method of defuzzification of output parameters from the base of fuzzy rules for a Mamdani fuzzy controller is given in the paper. The peculiarity of the method is the usage of the universal equation for the area computation of the geometric shapes. During the realization of fuzzy inference linguistic terms, the structure changes from the triangular into a trapezoidal shape. That is why the universal equation is used. The method is limited and can be used only for the triangular and trapezoidal membership functions. Gaussian functions can also be used while modifying the proposed method. Traditional defuzzification models such as Middle of Maxima − MoM, First of Maxima − FoM, Last of Maxima − LoM, First of Suppport − FoS, Last of Support − LoS, Middle of Support − MoS, Center of Sums − CoS, Model of Height − MoH have a number of systematic errors: curse of dimensionality, partition of unity condition and absence of additivity. The above-mentioned methods can be seen as Center of Gravity − CoG, which has the same errors. These errors lead to the fact that accuracy of fuzzy systems decreases, because during the training root mean square error increases. One of the reasons that provokes the errors is that some of the activated fuzzy rules are excluded from the fuzzy inference. It is also possible to increase the accuracy of the fuzzy system through properties of continuity. The proposed method guarantees fulfilling of the property of continuity, as the intersection point of the adjustment linguistic terms equals 0.5 when a parametrized membership function is used. The causes of errors and a way to delete them are reviewed in the paper. The proposed method excludes errors which are inherent to the traditional and non- traditional models of defuzzification. Comparative analysis of the proposed method of defuzzification with traditional and non-traditional models shows its effectiveness.
      Graphical abstract image

      PubDate: 2017-06-02T02:21:18Z
      DOI: 10.1016/j.asoc.2017.05.040
      Issue No: Vol. 59 (2017)
       
  • Multi-objective differential evolution with performance-metric-based
           self-adaptive mutation operator for chemical and biochemical dynamic
           optimization problems
    • Authors: Qinqin Fan; Weili Wang; Xuefeng Yan
      Pages: 33 - 44
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Qinqin Fan, Weili Wang, Xuefeng Yan
      Each mutation operator of differential evolution (DE) algorithm is generally suitable for certain specific types of multi-objective optimization problems (MOPs) or particular stages of the evolution. To automatically select an appropriate mutation operator for solving MOPs in different phases of the evolution, a multi-objective differential evolution with performance-metric-based self-adaptive mutation operator (MODE-PMSMO) is proposed in this study. In MODE-PMSMO, a modified inverted generational distance (IGD) is utilized to evaluate the performance of each mutation operator and guide the evolution of mutation operators. The proposed MODE-PMSMO is then compared with seven multi-objective evolutionary algorithms (MOEAs) on five bi-objective and five tri-objective optimization problems. Generally, MODE-PMSMO exhibits the best average performance among all compared algorithms on ten MOPs. Additionally, MODE-PMSMO is employed to solve four typical multi-objective dynamic optimization problems in chemical and biochemical processes. Experimental results indicate that MODE-PMSMO is suitable for solving these actual problems and can provide a set of nondominated solutions for references of decision makers.
      Graphical abstract image

      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.05.044
      Issue No: Vol. 59 (2017)
       
  • Distributed generation allocation with on-load tap changer on radial
           distribution networks using adaptive genetic algorithm
    • Authors: Sanjib Ganguly; Dipanjan Samajpati
      Pages: 45 - 67
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Sanjib Ganguly, Dipanjan Samajpati
      This paper presents a distribution generation (DG) allocation strategy along with on-load tap changer (OLTC) for radial distribution networks using adaptive genetic algorithm (AGA). The optimal locations for DG units and OLTC, the optimal generation for the DG units, and the OLTC tap ratio are determined by minimizing a weighted objective function comprising of network power loss and maximum bus voltage deviation. A planning model incorporating DG and OLTC models is devised. Three different types of DG units, i.e., DG operating at lagging, leading, and unity power factor are used in the planning. Two new AGA variants based on adaptively varying crossover and mutation probabilities are proposed and used in this work as solution strategies and their performances are compared with some of the existing similar type of AGA variants using the results of multiple runs. The performances of the proposed AGA variants are found to be better in terms of quality of the solutions and consistency. The results also show that the combined operation of DG and OLTC reduces significant amount of power loss and bus voltage deviation. A direct performance comparison with some of the similar approaches shows that the solutions obtained with the proposed approach provide similar amount of power loss reduction and bus voltage improvement with lesser amount of DG penetration level. The approach is demonstrated on a 69-bus test distribution network and a 52-bus Indian practical distribution network.
      Graphical abstract image

      PubDate: 2017-06-02T02:21:18Z
      DOI: 10.1016/j.asoc.2017.05.041
      Issue No: Vol. 59 (2017)
       
  • A hierarchical global path planning approach for mobile robots based on
           multi-objective particle swarm optimization
    • Authors: Thi Thoa Mac; Cosmin Copot; Duc Trung Tran; Robin De Keyser
      Pages: 68 - 76
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Thi Thoa Mac, Cosmin Copot, Duc Trung Tran, Robin De Keyser
      In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach.
      Graphical abstract image

      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.05.012
      Issue No: Vol. 59 (2017)
       
  • A study of overfitting in optimization of a manufacturing quality control
           procedure
    • Authors: Tea Tušar; Klemen Gantar; Valentin Koblar; Bernard Ženko; Bogdan Filipič
      Pages: 77 - 87
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Tea Tušar, Klemen Gantar, Valentin Koblar, Bernard Ženko, Bogdan Filipič
      Quality control of the commutator manufacturing process can be automated by means of a machine learning model that can predict the quality of commutators as they are being manufactured. Such a model can be constructed by combining machine vision, machine learning and evolutionary optimization techniques. In this procedure, optimization is used to minimize the model error, which is estimated using single cross-validation. This work exposes the overfitting that emerges in such optimization. Overfitting is shown for three machine learning methods with different sensitivity to it (trees, additionally pruned trees and random forests) and assessed in two ways (repeated cross-validation and validation on a set of unseen instances). Results on two distinct quality control problems show that optimization amplifies overfitting, i.e., the single cross-validation error estimate for the optimized models is overly optimistic. Nevertheless, minimization of the error estimate by single cross-validation in general results in minimization of the other error estimates as well, showing that optimization is indeed beneficial in this context.
      Graphical abstract image

      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.05.027
      Issue No: Vol. 59 (2017)
       
  • An improved MOEA/D algorithm for multi-objective multicast routing with
           network coding
    • Authors: Huanlai Xing; Zhaoyuan Wang; Tianrui Li; Hui Li; Rong Qu
      Pages: 88 - 103
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Huanlai Xing, Zhaoyuan Wang, Tianrui Li, Hui Li, Rong Qu
      Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time.
      Graphical abstract image

      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.05.033
      Issue No: Vol. 59 (2017)
       
  • Prey predator hyperheuristic
    • Authors: Surafel Luleseged Tilahun
      Pages: 104 - 114
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Surafel Luleseged Tilahun
      Prey predator algorithm is a population based metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, a solution with a better performance is called best prey and focuses totally on exploitation whereas the solution with least performance is called predator and focuses totally on exploration. The remaining solutions are called ordinary prey and either exploit promising regions by following better performing solutions or explore the solution space by randomly running away from the predator. Recently, it has been shown that by increasing the number of best prey or predator, it is possible to adjust the degree of exploitation and exploration. Even though, this tuning has the advantage of easily controlling these search behaviors, it is not an easy task. As any other metaheuristic algorithm, the performance of prey predator algorithm depends on the proper degree of exploration and exploitation of the decision space. In this paper, the concept of hyperheuristic is employed to balance the degree of exploration and exploitation of the algorithm. So that it learns and decides the best search behavior for the problem at hand in iterations. The ratio of the number of the best prey and the predators are used as low level heuristics. From the simulation results the balancing of the degree of exploration and exploitation by using hyperheuristic mechanism indeed improves the performance of the algorithm. Comparison with other algorithms shows the effectiveness of the proposed approach.
      Graphical abstract image

      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.04.044
      Issue No: Vol. 59 (2017)
       
  • Safety management of waterway congestions under dynamic risk
           conditions—A case study of the Yangtze River
    • Authors: X.P. Yan; C.P. Wan; D. Zhang; Z.L. Yang
      Pages: 115 - 128
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): X.P. Yan, C.P. Wan, D. Zhang, Z.L. Yang
      With the continuous increase of traffic volume in recent years, inland waterway transportation suffers more and more from congestion problems, which form a major impediment to its development. Thus, it is of great significance for the stakeholders and decision makers to address these congestion issues properly. Fuzzy Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) is widely used for solving Multiple Criteria Decision Making (MCDM) problems with ambiguity. When taking into account fuzzy TOPSIS, decisions are made in a static scenario with fixed weights assigned to the criteria. However, risk conditions usually vary in real-life cases, which will inevitably affect the preference ranking of the alternatives. To make flexible decisions according to the dynamics of congestion risks and to achieve a rational risk analysis for prioritising congestion risk control options (RCOs), the cost-benefit ratio (CBR) is used in this paper to reflect the change of risk conditions. The hybrid of CBR and fuzzy TOPSIS is illustrated by investigating the congestion risks of the Yangtze River. The ranking of RCOs varies depending on the scenarios with different congestion risk conditions. The research findings indicate that some RCOs (e.g. “Channel dredging and maintenance”, and “Prohibition of navigation”) are more cost effective in the situation of a high level of congestion risk, while the other RCOs (e.g. “Loading restriction”, and “Crew management and training”) are more beneficial in a relatively low congestion risk condition. The proposed methods and the evaluation results provide useful insights for effective safety management of the inland waterway congestions under dynamic risk conditions.

      PubDate: 2017-06-06T16:00:50Z
      DOI: 10.1016/j.asoc.2017.05.053
      Issue No: Vol. 59 (2017)
       
  • Two-phase optimization for support vectors and parameter selection of
           support vector machines: Two-class classification
    • Authors: Shinq-Jen Wu; Van-Hung Pham; Thi-Nga Nguyen
      Pages: 129 - 142
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Shinq-Jen Wu, Van-Hung Pham, Thi-Nga Nguyen
      Support vector machines (SVMs) are one of the most popular classification tools and show the most potential to address under-sampled noisy data (a large number of features and a relatively small number of samples). However, the computational cost is too expensive, even for modern-scale samples, and the performance largely depends on the proper setting of parameters. As the data scale increases, the improvement in speed becomes increasingly challenging. As the dimension (feature number) largely increases while the sample size remains small, the avoidance of overfitting becomes a significant challenge. In this study, we propose a two-phase sequential minimal optimization (TSMO) to largely reduce the training cost for large-scale data (tested with 3186–70,000-sample datasets) and a two-phased-in differential-learning particle swarm optimization (tDPSO) to ensure the accuracy for under-sampled data (tested with 2000–24481-feature datasets). Because the purpose of training SVMs is to identify support vectors that denote a hyperplane, TSMO is developed to quickly select support vector candidates from the entire dataset and then identify support vectors from those candidates. In this manner, the computational burden is largely reduced (a 29.4%–65.3% reduction rate). The proposed tDPSO uses topology variation and differential learning to solve PSO’s premature convergence issue. Population diversity is ensured through dynamic topology until a ring connection is achieved (topology-variation phases). Further, particles initiate chemo-type simulated-annealing operations, and the global-best particle takes a two-turn diversion in response to stagnation (event-induced phases). The proposed tDPSO-embedded SVMs were tested with several under-sampled noisy cancer datasets and showed superior performance over various methods, even those methods with feature selection for the preprocessing of data.
      Graphical abstract image

      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.05.021
      Issue No: Vol. 59 (2017)
       
  • A group decision-making model based on incomplete comparative expressions
           with hesitant linguistic terms
    • Authors: Yongming Song; Jun Hu
      Pages: 174 - 181
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Yongming Song, Jun Hu
      As a result of uncertainty and complexity for environments of decision-making, it is more suitable for decision makers to use hesitant fuzzy linguistic information. In this paper, a novel group decision making (GDM) model based on fuzzy linear programming is proposed for incomplete comparative expressions with hesitant fuzzy linguistic term set (HFLTSs). We establish an equivalence theorem of additive consistency between 2-tuple fuzzy linguistic preference relation (FLPR) and corresponding fuzzy preference relation. Based on this framework, a fuzzy linear programming is established to address incomplete comparative expressions with HFLTSs. It is more important that the proposed fuzzy linear programming has a double action, finding the highest consistent incomplete 2-tuple FLPR and increasing inconsistent 2-tuple FLPR to the additive consistent 2-tuple FLPR based on given incomplete comparative expressions with HFLTSs. By this means, a novel GDM model is constructed based on importance induced ordered weighted averaging operator. Finally, an investment decision-making in real-world is solved by the proposed model, which shows the result of GDM is effectiveness.

      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.05.056
      Issue No: Vol. 59 (2017)
       
  • A time-varying transfer function for balancing the exploration and
           exploitation ability of a binary PSO
    • Authors: Md. Jakirul Islam; Xiaodong Li; Yi Mei
      Pages: 182 - 196
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Md. Jakirul Islam, Xiaodong Li, Yi Mei
      Many real-world problems belong to the family of discrete optimization problems. Most of these problems are NP-hard and difficult to solve efficiently using classical linear and convex optimization methods. In addition, the computational difficulties of these optimization tasks increase rapidly with the increasing number of decision variables. A further difficulty can be also caused by the search space being intrinsically multimodal and non-convex. In such a case, it is more desirable to have an effective optimization method that can cope better with these problem characteristics. Binary particle swarm optimization (BPSO) is a simple and effective discrete optimization method. The original BPSO and its variants have been used to solve a number of classic discrete optimization problems. However, it is reported that the original BPSO and its variants are unable to provide satisfactory results due to the use of inappropriate transfer functions. More specifically, these transfer functions are unable to provide BPSO a good balance between exploration and exploitation in the search space, limiting their performances. To overcome this problem, this paper proposes to employ a time-varying transfer function in the BPSO, namely TV T -BPSO. To understand the search behaviour of the TV T -BPSO, we provide a systematic analysis of its exploration and exploitation capability. Our experimental results demonstrate that TV T -BPSO outperforms existing BPSO variants on both low-dimensional and high-dimensional classical 0–1 knapsack problems, as well as a 200-member truss problem, suggesting that TV T -BPSO is able to better scale to high dimensional combinatorial problems than the existing BPSO variants and other metaheuristic algorithms.
      Graphical abstract image

      PubDate: 2017-06-12T06:00:37Z
      DOI: 10.1016/j.asoc.2017.04.050
      Issue No: Vol. 59 (2017)
       
  • Recognition of power quality disturbances using S-transform based ruled
           decision tree and fuzzy C-means clustering classifiers
    • Authors: Om Prakash Mahela; Abdul Gafoor Shaik
      Pages: 243 - 257
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Om Prakash Mahela, Abdul Gafoor Shaik
      A method based on Stockwell's transform and Fuzzy C-means (FCM) clustering initialized by decision tree has been proposed in this paper for detection and classification of power quality (PQ) disturbances. Performance of this method is compared with S-transform based ruled decision tree. PQ disturbances are simulated in conformity with standard IEEE-1159 using MATLAB software. Different statistical features of PQ disturbance signals are obtained using Stockwell's transform based multi-resolution analysis of signals. These features are given as input to the proposed techniques such as rule-based decision tree and FCM clustering initialized by ruled decision tree for classification of various PQ disturbances. The PQ disturbances investigated in this study include voltage swell, voltage sag, interruption, notch, harmonics, spike, flicker, impulsive transient and oscillatory transient. It has been observed that the efficiency of classification based on ruled decision tree deteriorates in the presence of noise. However, the classification based on Fuzzy C-means clustering initialized by decision tree gives results with high accuracy even in the noisy environment. Validity of simulation results has been verified through comparisons with results in real time obtained using the Real Time Digital Simulator (RTDS) in hardware synchronization mode. The proposed algorithm is established effectively by results of high accuracy to detect and classify various electrical power quality disturbances.
      Graphical abstract image

      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.05.061
      Issue No: Vol. 59 (2017)
       
  • A parallel double-level multiobjective evolutionary algorithm for robust
           optimization
    • Authors: Wei-Jie Yu; Jin-Zhou Li; Wei-Neng Chen; Jun Zhang
      Pages: 258 - 275
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Wei-Jie Yu, Jin-Zhou Li, Wei-Neng Chen, Jun Zhang
      Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.

      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.06.008
      Issue No: Vol. 59 (2017)
       
  • A jumping genes inspired multi-objective differential evolution algorithm
           for microwave components optimization problems
    • Authors: Shao Yong Zheng; Sheng Xin Zhang
      Pages: 276 - 287
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Shao Yong Zheng, Sheng Xin Zhang
      Exploitation and exploration are equally important for multi-objective evolutionary algorithms (MOEAs) to approximate the optimal Pareto Front (PF). However, most existing multi-objective differential evolution algorithms (MODEs) focus on exploitation with proposals of elitism methods that may result in poor diversity or sticking into local optimal front. Inspired by a biological discovery, this study proposes a jumping genes based MODE algorithm, termed as JGDE with two components. The first component is the application of jumping genes operator to MODE to promote population diversity while in the second component, an elitism leading mechanism is designed to accelerate the convergence. Experimental studies show the superiority of JGDE over other competitive algorithms in both convergence and diversity. More importantly, JGDE can deal with local optimal fronts that are difficult to handle by the existing MODEs. The studies are also further verified by the solvement of a practical microwave components optimization problem and the comparison between different optimization algorithms.
      Graphical abstract image

      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.05.062
      Issue No: Vol. 59 (2017)
       
  • Study on an improved adaptive PSO algorithm for solving multi-objective
           gate assignment
    • Authors: Wu Deng; Huimin Zhao; Xinhua Yang; Juxia Xiong; Meng Sun; Bo Li
      Pages: 288 - 302
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Wu Deng, Huimin Zhao, Xinhua Yang, Juxia Xiong, Meng Sun, Bo Li
      Gate is a key resource in the airport, which can realize rapid and safe docking, ensure the effective connection between flights and improve the capacity and service efficiency of airport. The minimum walking distances of passengers, the minimum idle time variance of each gate, the minimum number of flights at parking apron and the most reasonable utilization of large gates are selected as the optimization objectives, then an efficient multi-objective optimization model of gate assignment problem is proposed in this paper. Then an improved adaptive particle swarm optimization(DOADAPO) algorithm based on making full use of the advantages of Alpha-stable distribution and dynamic fractional calculus is deeply studied. The dynamic fractional calculus with memory characteristic is used to reflect the trajectory information of particle updating in order to improve the convergence speed. The Alpha-stable distribution theory is used to replace the uniform distribution in order to escape from the local minima in a certain probability and improve the global search ability. Next, the DOADAPO algorithm is used to solve the constructed multi-objective optimization model of gate assignment in order to fast and effectively assign the gates to different flights in different time. Finally, the actual flight data in one domestic airport is used to verify the effectiveness of the proposed method. The experiment results show that the DOADAPO algorithm can improve the convergence speed and enhance the local search ability and global search ability, and the multi-objective optimization model of gate assignment can improve the comprehensive service of gate assignment. It can effectively provide a valuable reference for assigning the gates in hub airport.
      Graphical abstract image

      PubDate: 2017-06-16T07:54:42Z
      DOI: 10.1016/j.asoc.2017.06.004
      Issue No: Vol. 59 (2017)
       
  • A hybrid EDA for load balancing in multicast with network coding
    • Authors: Huanlai Xing; Saifei Li; Yunhe Cui; Lianshan Yan; Wei Pan; Rong Qu
      Pages: 363 - 377
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Huanlai Xing, Saifei Li, Yunhe Cui, Lianshan Yan, Wei Pan, Rong Qu
      Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms.

      PubDate: 2017-06-21T14:33:54Z
      DOI: 10.1016/j.asoc.2017.06.003
      Issue No: Vol. 59 (2017)
       
  • Space-decomposition based 3D fuzzy control design for nonlinear spatially
           distributed systems with multiple control sources using multiple
           single-output SVR learning
    • Authors: Xian-Xia Zhang; Lian-rong Zhao; Jia-jia Li; Gui-tao Cao; Bing Wang
      Pages: 378 - 388
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Xian-Xia Zhang, Lian-rong Zhao, Jia-jia Li, Gui-tao Cao, Bing Wang
      Three-dimensional fuzzy logic controller (3D FLC) is a recently developed FLC integrating space information expression and processing for nonlinear spatially distributed dynamical systems (SDDSs). Like a traditional FLC, expert knowledge can help design a 3D FLC. Nevertheless, there are some situations where expert knowledge cannot be formulated into precise words; what's worse, it might not be explicitly expressed in words. In contrast, spatio-temporal data sets containing control laws are usually available. In this study, a data-driven based 3D FLC design method using multiple single-output support vector regressions (SVRs) is proposed for SDDSs with multiple control sources. Firstly, in terms of the locally spatial influence feature of control sources on the space domain, a complex SDDS is decomposed into multiple SDDSs with one control source and a space-decomposition based 3D fuzzy control scheme is proposed. Secondly, multiple single-output SVRs with ε-insensitive cost function are used to learn and design multiple 3D FLCs from spatio-temporal data sets. Thirdly, a five-step design scheme is proposed, including space decomposition, data collection, spatial support-vector learning, 3D fuzzy rule construction, and 3D fuzzy controller integration. Finally, the proposed method is applied to a packed-bed reactor and simulation results were used to verify its effectiveness.

      PubDate: 2017-06-21T14:33:54Z
      DOI: 10.1016/j.asoc.2017.04.064
      Issue No: Vol. 59 (2017)
       
  • Back propagation genetic and recurrent neural network applications in
           modelling and analysis of squeeze casting process
    • Authors: Manjunath Patel G.C; Arun Kumar Shettigar; Prasad Krishna; Mahesh B. Parappagoudar
      Pages: 418 - 437
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Manjunath Patel G.C, Arun Kumar Shettigar, Prasad Krishna, Mahesh B. Parappagoudar
      Today, in competitive manufacturing environment reducing casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in squeeze casting process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of casting variables (that is, squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of casting variables for the required casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and précised control of squeeze casting process.
      Graphical abstract image

      PubDate: 2017-06-21T14:33:54Z
      DOI: 10.1016/j.asoc.2017.06.018
      Issue No: Vol. 59 (2017)
       
  • Semi-supervised learning using hidden feature augmentation
    • Authors: Wenlong Hang; Kup-Sze Choi; Shitong Wang; Pengjiang Qian
      Pages: 448 - 461
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): Wenlong Hang, Kup-Sze Choi, Shitong Wang, Pengjiang Qian
      Semi-supervised learning methods are conventionally conducted by simultaneously utilizing abundant unlabeled samples and a few labeled samples given. However, the unlabeled samples are usually adopted with assumptions, e.g., cluster and manifold assumptions, which degrade the performance when the assumptions become invalid. The reliable hidden features embedded in both the labeled and the unlabeled samples can potentially be used to tackle this issue. In this regard, we investigate the feature augmentation technique to improve the robustness of semi-supervised learning in this paper. By introducing an orthonormal projection matrix, we first transform both the unlabeled and labeled samples into a shared hidden subspace to determine the connections between the samples. Then we utilize the hidden features, the raw features, and zero vectors determined to develop a novel feature augmentation strategy. Finally, a hidden feature transformation (HTF) model is proposed to compute the desired projection matrix by applying the maximum joint probability distribution principle in the augmented feature space. The effectiveness of the proposed method is evaluated in terms of the hinge and square loss functions respectively, based on two types of semi-supervised classification formulations developed using only the labeled samples with their original features and hidden features. The experimental results have demonstrated the effectiveness of the proposed feature augmentation technique for semi-supervised learning.

      PubDate: 2017-06-21T14:33:54Z
      DOI: 10.1016/j.asoc.2017.06.017
      Issue No: Vol. 59 (2017)
       
  • 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.
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      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 Kriging-assisted multiobjective evolutionary algorithm
    • Authors: Giovanni Venturelli; Ernesto Benini; Łukasz Łaniewski-Wołłk
      Pages: 155 - 175
      Abstract: Publication date: September 2017
      Source:Applied Soft Computing, Volume 58
      Author(s): Giovanni Venturelli, Ernesto Benini, Łukasz Łaniewski-Wołłk
      A surrogate-assisted (SA) evolutionary algorithm for Multiobjective Optimization Problems (MOOPs) is presented as a contribution to Soft Computing (SC) in Artificial Intelligence (AI). Such algorithm is grounded on the cooperation between a “pure” evolutionary algorithm and a Kriging based algorithm featuring the Expected Hyper-Volume Improvement (EHVI) metric. Comparison with state-of-art pure and Kriging-assisted algorithms over two- and three-objective test functions have demonstrated that the proposed algorithm can achieve high performance in the approximation of the Pareto-optimal front mitigating the drawbacks of its parent algorithms.

      PubDate: 2017-05-28T02:16:01Z
      DOI: 10.1016/j.asoc.2017.04.017
      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)
       
  • 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)
       
  • UFuzzy: Fuzzy models with Universum
    • Authors: L. Tencer; M. Reznakova; M. Cheriet
      Pages: 1296 - 1315
      Abstract: Publication date: October 2017
      Source:Applied Soft Computing, Volume 59
      Author(s): L. Tencer, M. Reznakova, M. Cheriet
      Recently, many works have begun to explore the possibility of using samples out of the training set to improve their results. One of these approaches, Universum, introduced by Vapnik had already been used in combination with several classifiers to increase their accuracy. In this paper, we present a novel approach for identifying the consequent parameters of the Takagi-Sugeno Fuzzy Model, U Fuzzy. It is based on the idea of the Universum set, which acts to regularize the optimization problem. It also helps with the introduction of prior knowledge to the tasks performed by the model. In addition, we explore the influence of the Universum set on identifying the structure of fuzzy rules using the c-means clustering algorithm. We evaluated our approach on several generated and real-world classification datasets and it shows promising results in comparison to the baseline methods, which do not use the Universum set.

      PubDate: 2017-06-02T02:21:18Z
      DOI: 10.1016/j.asoc.2016.05.041
      Issue No: Vol. 52 (2017)
       
  • 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)
       
  • 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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