Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique |
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| Authors: | Wei-feng Gao San-yang Liu Ling-ling Huang |
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| Affiliation: | 1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China;2. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt;3. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI Av. Revolucion 1500, Guadalajara, Jal, Mexico;4. Cybernetics Institute, Tomsk Polytechnic University, Lenin Avenue 30, Tomsk, Russian Federation;5. Hubei Collaborative Innovation Center of Basic Education Information Technology Services, Hubei University of Education, Wuhan, China;1. University of Bisha, Saudi Arabia;2. Department of Computer, Damietta University, Egypt;3. Department of Mathematics, Faculty of Science, Zagazig University, Egypt;4. Faculty of Computers and Information, Minia University, Egypt;1. Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia;2. School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA;3. School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia;4. Business Information Technology Department, King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan;5. Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, H3G1M8, Canada;6. BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 488241, USA |
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| Abstract: | Particle swarm optimization (PSO) is a relatively new optimization algorithm that has been applied to a variety of problems. However, it may easily get trapped in a local optima when solving complex multimodal problems. To address this concerning issue, we propose a novel PSO called as CSPSO to improve the performance of PSO on complex multimodal problems in the paper. Specifically, a stochastic search technique is used to execute the exploration in PSO, so as to help the algorithm to jump out of the likely local optima. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. Moreover, numerical simulation and comparisons with some typical existing algorithms demonstrate the superiority of the proposed algorithm. |
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