Journal
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
Volume 17, Issue 11, Pages 4316-4327Publisher
ELSEVIER
DOI: 10.1016/j.cnsns.2012.03.015
Keywords
Particle swarm optimization; Opposition-based learning method; Chaotic maps; Stochastic search technique; Initialization approach
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Funding
- National Nature Science Foundation of China [60974082, 11126187]
- Fundamental Research Funds for the Central Universities [K50510700004]
- Foundation of State Key Lab. of Integrated Services Networks of China
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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. (C) 2012 Elsevier B.V. All rights reserved.
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