期刊
APPLIED SOFT COMPUTING
卷 29, 期 -, 页码 169-183出版社
ELSEVIER
DOI: 10.1016/j.asoc.2014.12.026
关键词
Particle swarm optimizer; Dynamic multi-swarm particle swarm optimizer; Cooperative learning strategy
资金
- National Natural Science Foundation of China [61273260, 61290322, 61273222, 61322303]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20121333120010]
- Natural Science Foundation of Hebei Province [F2014203267]
- Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, PR China [SCIP2012008]
In this article, the dynamic multi-swarm particle swarm optimizer (DMS-PSO) and a new cooperative learning strategy (CLS) are hybridized to obtain DMS-PSO-CLS. DMS-PSO is a recently developed multi-swarm optimization algorithm and has strong exploration ability for the use of a novel randomly regrouping schedule. However, the frequently regrouping operation of DMS-PSO results in the deficiency of the exploitation ability. In order to achieve a good balance between the exploration and exploitation abilities, the cooperative learning strategy is hybridized to DMS-PSO, which makes information be used more effectively to generate better quality solutions. In the proposed strategy, for each sub-swarm, each dimension of the two worst particles learns from the better particle of two randomly selected sub-swarms using tournament selection strategy, so that particles can have more excellent exemplars to learn and can find the global optimum more easily. Experiments are conducted on some well-known benchmarks and the results show that DMS-PSO-CLS has a superior performance in comparison with DMS-PSO and several other popular PSO variants. (C) 2014 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据