Particle Swarm Optimization algorithm with multiple social learning structures
Pisut Pongchairerks and
Voratas Kachitvichyanukul
International Journal of Operational Research, 2009, vol. 6, issue 2, 176-194
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. (search for similar items in EconPapers)
Date: 2009
References: Add references at CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.inderscience.com/link.php?id=26534 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijores:v:6:y:2009:i:2:p:176-194
Access Statistics for this article
More articles in International Journal of Operational Research from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().