Stepwise strategies in particle swarm optimization

被引：2
作者：
Hu, Jian [1 ]
Li, Zhi-Shu [1 ]
Ou, Peng [1 ]
Luo, Si-Da [1 ]
机构：
[1] College of Computer Science, Sichuan University
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2009年 / 38卷 / 03期
关键词：
Convergence; Evaluation strategy; Evolutionary algorithms; Learning strategy; Particle swarm optimization; Stepwise strategy; Swarm intelligence;
D O I：
10.3969/j.issn.1001-0548.2009.03.028
中图分类号：
学科分类号：
摘要：
The particle swarm optimization (PSO) may be trapped in local optima and fail to converge to global optima, especially for multimodal and high-dimensional problems. To handle this problem, a stepwise learning strategy and a stepwise evaluation strategy are presented. The former makes each particle learn from only one particle's historical best information in each update progress in order to search in a potential area, and simplifies particles' update rules to easily control their convergence behaviors. The latter enables each particle to be evaluated in dimension-by-dimension order so as to step steadily toward the destination position, and settles non-separable problems by means of detecting relationships between dimensions. Application of the new PSO on several benchmark optimization problems shows a marked improvement in performance over six other recent variants of the PSO.
引用
收藏
页码：435 / 439
页数：4
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