A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems |
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| Affiliation: | 1. College of Information Technology, Heilongjiang Bayi Agricultural University, Daqing 163319, PR China;2. Intelligent System & Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK;3. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, Fujian 361005, PR China;4. Department of Mathematics, Yangzhou University, Yangzhou, Jiangsu 225002, PR China;5. Communications Systems and Networks (CSN) Research Group, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. Department of Information and Communication Engineering, Chaoyang University of Technology, 168, Jifeng E. Rd., Wufeng District, Taichung City 41349, Taiwan;2. Department of Electrical Engineering, National Formosa University, 64, Wunhua Rd., Huwei Township, Yunlin County 63201, Taiwan |
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| Abstract: | In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations. |
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| Keywords: | Particle swarm optimization (PSO) Multiple subpopulations Multimodal optimization problem |
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