Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique
- 作者：Wei-feng Gao ; gaoweifeng2004@126.com ; San-yang Liu ; Ling-ling Huang
- 关键词：Particle swarm optimization ; Opposition-based learning method ; Chaotic maps ; Stochastic search technique ; Initialization approach
- 刊名：Communications in Nonlinear Science and Numerical Simulation
- 出版年：2012
- 期刊代码：89_10075704
- 类别：ph
- 出版时间：November, 2012
- 卷：17
- 期：11
- 页码：4316-4327
- 文件大小：543 K
摘要
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.