Authors: Pisut Pongchairerks, Voratas Kachitvichyanukul
Addresses: Industrial Engineering Programme, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 12121, Thailand. ' Industrial Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, 12120, Thailand
Abstract: This paper proposes a variant of Particle Swarm Optimisation (PSO) algorithm which enhances the social learning structure of the standard PSO by incorporating multiple social best positions. The research in this paper analyses the effects of main parameters on the proposed algorithm|s performance by using factorial experiment. To verify the research findings, this paper compares the proposed algorithm|s performance to those of several well-known PSO algorithms. Eventually, the comparison results indicate that the proposed algorithm outperforms others.
Keywords: PSO; particle swarm optimisation; neighbourhood; factorial experiment; social learning structures.
International Journal of Operational Research, 2009 Vol.6 No.2, pp.176 - 194
Published online: 19 Jun 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article