A novel particle swarm optimizer using optimal foraging theory

Authors:
Ben Niu;Yunlong Zhu;Kunyuan Hu;Sufen Li;Xiaoxian He
Affiliations:
,Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China;,Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Year:
2006

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Abstract

Based on the research of optimal foraging theory (OFT), we present a novel particle swarm optimizer (PSO) to improve the performance of standard PSO (SPSO). The resulting algorithm is known as PSOOFT that makes use of two mechanisms of OFT: a reproduction strategy to enhance the ability to converge rapidly to good solutions and a patch-choice based scheme to keep a right balance of exploration and exploitation. In the simulation studies, several benchmark functions are performed, and the performance of the proposed algorithm is compared to the standard PSO (SPSO). The experimental results show that the PSOOFT prevents premature convergence to a high degree, but still has a more rapid convergence rate than SPSO.