Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems. (11th April 2013)

• Record Type: Journal Article
• Authors: Wang, J. W.; Wang, H. F.; Ip, W. H.; Furuta, K.; Kanno, T.; Zhang, W. J.
• Other Names: Bi Zhuming Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-04-11
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/749256
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.