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Article: Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems

TitlePredatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems
Authors
Issue Date2013
PublisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/mpe/index.html
Citation
Mathematical Problems in Engineering: theory, methods and applications, 2013, v. 2013, article no. 749256 How to Cite?
AbstractWe 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. © 2013 J. W. Wang et al.
Persistent Identifierhttp://hdl.handle.net/10722/198485
ISSN
2015 Impact Factor: 0.644
2015 SCImago Journal Rankings: 0.285
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, JWen_US
dc.contributor.authorWang, HFen_US
dc.contributor.authorIp, WHen_US
dc.contributor.authorFuruta, Ken_US
dc.contributor.authorKanno, Ten_US
dc.contributor.authorZhang, WJen_US
dc.date.accessioned2014-07-07T07:12:49Z-
dc.date.available2014-07-07T07:12:49Z-
dc.date.issued2013en_US
dc.identifier.citationMathematical Problems in Engineering: theory, methods and applications, 2013, v. 2013, article no. 749256en_US
dc.identifier.issn1024-123X-
dc.identifier.urihttp://hdl.handle.net/10722/198485-
dc.description.abstractWe 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. © 2013 J. W. Wang et al.-
dc.languageengen_US
dc.publisherHindawi Publishing Corporation. The Journal's web site is located at http://www.hindawi.com/journals/mpe/index.html-
dc.relation.ispartofMathematical Problems in Engineering: theory, methods and applicationsen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titlePredatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problemsen_US
dc.typeArticleen_US
dc.identifier.emailWang, JW: jwwang@hku.hken_US
dc.identifier.authorityWang, JW=rp01888en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1155/2013/749256en_US
dc.identifier.scopuseid_2-s2.0-84877260335-
dc.identifier.hkuros229715en_US
dc.identifier.volume2013en_US
dc.identifier.isiWOS:000317767400001-
dc.publisher.placeUnited States-

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