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Article: Particle swarm optimization with a leader and followers

TitleParticle swarm optimization with a leader and followers
Authors
KeywordsParticle swarm optimization
Goose team optimization
Role division
Parallel principle
Aggregate principle
Issue Date2008
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/726366/description#description
Citation
Progress in Natural Science: materials international , 2008, v. 18 n. 11, p. 1437-1443 How to Cite?
AbstractReferring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO, a team is formed by the particles with a leader and some followers. The role of the leader is to determine the search direction. The followers decide their flying modes according to their distances to the leader individually. Thus, a wide area can be explored and the particle collision can be really avoided. When GTO is applied to four benchmark examples of complex nonlinear functions, it has a better computation performance than the standard PSO.
Persistent Identifierhttp://hdl.handle.net/10722/222784
ISSN
2015 Impact Factor: 1.753
2015 SCImago Journal Rankings: 0.575

 

DC FieldValueLanguage
dc.contributor.authorWang, J-
dc.contributor.authorWang, D-
dc.date.accessioned2016-01-28T06:54:54Z-
dc.date.available2016-01-28T06:54:54Z-
dc.date.issued2008-
dc.identifier.citationProgress in Natural Science: materials international , 2008, v. 18 n. 11, p. 1437-1443-
dc.identifier.issn1002-0071-
dc.identifier.urihttp://hdl.handle.net/10722/222784-
dc.description.abstractReferring to the flight mechanism of wild goose flock, we propose a novel version of Particle Swarm Optimization (PSO) with a leader and followers. It is referred to as Goose Team Optimization (GTO). The basic features of goose team flight such as goose role division, parallel principle, aggregate principle and separate principle are implemented in the recommended algorithm. In GTO, a team is formed by the particles with a leader and some followers. The role of the leader is to determine the search direction. The followers decide their flying modes according to their distances to the leader individually. Thus, a wide area can be explored and the particle collision can be really avoided. When GTO is applied to four benchmark examples of complex nonlinear functions, it has a better computation performance than the standard PSO.-
dc.languageeng-
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/726366/description#description-
dc.relation.ispartofProgress in Natural Science: materials international-
dc.rights© 2008. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectParticle swarm optimization-
dc.subjectGoose team optimization-
dc.subjectRole division-
dc.subjectParallel principle-
dc.subjectAggregate principle-
dc.titleParticle swarm optimization with a leader and followers-
dc.typeArticle-
dc.identifier.emailWang, J: jwwang@hku.hk-
dc.identifier.authorityWang, J=rp01888-
dc.identifier.doidoi:10.1016/j.pnsc.2008.03.029-
dc.identifier.scopuseid_2-s2.0-56849126666-
dc.identifier.volume18-
dc.identifier.issue11-
dc.identifier.spage1437-
dc.identifier.epage1443-
dc.publisher.placeUnited Kingdom-

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