A hybrid particle swarm optimization algorithm based on space transformation search and a modified velocity model

Authors:
Song Yu;Zhijian Wu;Hui Wang;Zhangxing Chen
Affiliations:
State Key Lab of Software Engineering, Wuhan University, Wuhan, P.R. China;State Key Lab of Software Engineering, Wuhan University, Wuhan, P.R. China;State Key Lab of Software Engineering, Wuhan University, Wuhan, P.R. China;Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada
Venue:
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
Year:
2009

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Abstract

Particle Swarm Optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO often easily falls into local optima because the particles would quickly get closer to the best particle. Under these circumstances, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by combining space transformation search (STS) with a new modified velocity model. Experimental studies on 8 benchmark functions demonstrate that the HPSO holds good performance in solving both unimodal and multimodal functions optimization problems.