文献类型：期刊文献

英文题名：Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems

作者：Wang, J. W.[1,2,3];Wang, H. F.[4];Ip, W. H.[5];Furuta, K.[3];Kanno, T.[3];Zhang, W. J.[1,2]

机构：[1]E China Univ Sci & Technol, Complex Syst Res Ctr, Shanghai 200237, Peoples R China;[2]Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A9, Canada;[3]Univ Tokyo, Dept Syst Innovat, Tokyo 1138656, Japan;[4]Northeastern Univ, Inst Syst Engn, Shenyang 110114, Peoples R China;[5]Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China

年份：2013

卷号：2013

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20132016324294);WOS:【SCI-EXPANDED(收录号:WOS:000317767400001)】；

基金：The authors want to thank the financial support to this work by NSERC through a strategic project grant, and a grant of ECUST through a program of the Fundamental Research Funds for the Central Universities to W. J. Zhang, and by NSFC (Grant No. 71001018, 61273031) of China to H. F Wang. Our gratitude is also extended to the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University (G-YK04).

语种：英文

外文关键词：Particle swarm optimization (PSO)

摘要：We propose an approach to solve continuous variable optimization problems. The approach is based on the integration of predatory search strategy (PSS) and swarm intelligence technique. The integration is further based on two newly defined concepts proposed for the PSS, namely, "restriction" and "neighborhood," and takes the particle swarm optimization (PSO) algorithm as the local optimizer. The PSS is for the switch of exploitation and exploration (in particular by the adjustment of neighborhood), while the swarm intelligence technique is for searching the neighborhood. The proposed approach is thus named PSS-PSO. Five benchmarks are taken as test functions (including both unimodal and multimodal ones) to examine the effectiveness of the PSS-PSO with the seven well-known algorithms. The result of the test shows that the proposed approach PSS-PSO is superior to all the seven algorithms.