Title: An improved PSO with detecting and local-learning strategy

Authors: Xuewen Xia; Bo Wei; Chengwang Xie

Addresses: School of Software,East China Jiaotong University, Nanchang, China ' School of Software,East China Jiaotong University, Nanchang, China ' School of Software, East China Jiaotong University, Nanchang, China

Abstract: Particle swarm optimisation (PSO) has been applied to a variety of problems due to its simplicity of implement. However, the standard PSO suffers from premature convergence and slow global optimisation. This paper presents a novel PSO algorithm, in which detecting strategy and local-learning strategy are adopted to improve PSO's performance. In the new PSO algorithm, which is called DLPSO in this paper, search space of each dimension is divided into many equal subregions. According to statistical information of all particles' historical best position, the globally best particle can detect some inferior (or superior) subregions. In the local-learning strategy, the global best particle can carry out a local search during the later evolution process. The results of experiments show that the detecting strategy can act on the globally best particle to jump out of the likely local optimal solutions while local-learning strategy can help DLPSO obtain more accurate solutions. In addition, experimental results also demonstrate that DLPSO is more suitable for multimodal function optimisation while it has a comprehensive ability for function optimisation.

Keywords: particle swarm optimisation; PSO performance; detection strategy; local learning strategy; inferior subregions; superior subregions; local search.

DOI: 10.1504/IJCSM.2014.066445

International Journal of Computing Science and Mathematics, 2014 Vol.5 No.4, pp.370 - 380

Received: 12 Jul 2014
Accepted: 20 Aug 2014
Published online: 31 Jan 2015 *

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