A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model

摘要：

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.

关键词： Space Transformation Search (STS) evolutionary algorithm Particle Swarm Optimization (PSO) optimization

作者: Song Yu Zhijian Wu Hui Wang Zhangxing Chen

作者单位: State Key Lab of Software Engineering, Wuhan University,Wuhan 430072,P.R. China State Key Lab of Software Engineering, Wuhan University,Wuhan 430072, P.R. China Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calg

会议类型: 国际会议

会议名称: The Second International Conference on High Performance Computing and Applications(第二届高性能计算及应用国际会议)

会议地点: 上海

会议语种:英文

页码: 522-527

在线出版日期: 2009-08-10（万方平台首次上网日期，不代表论文的发表时间）