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Title: Predatory search strategy based on swarm intelligence for continuous optimization problems
Authors: Wang, JW
Wang, HF
Ip, WH 
Furuta, K
Kanno, T
Zhang, WJ
Issue Date: 2013
Publisher: Hindawi Publishing Corporation
Source: Mathematical problems in engineering, 2013, v. 2013, 749256, p. 1-11 How to cite?
Journal: Mathematical problems in engineering 
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.
ISSN: 1024-123X
EISSN: 1563-5147
DOI: 10.1155/2013/749256
Rights: Copyright © 2013 J.W.Wang et al. This is an open access article distributed under the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Wang, J. W., Wang, H. F., Ip, W. H., Furuta, K., Kanno, T., & Zhang, W. J. (2013). Predatory search strategy based on swarm intelligence for continuous optimization problems. Mathematical Problems in Engineering, 2013, 749256, 1-11 is available at
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