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dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorLewis, Andrew
dc.contributor.authorSadiq, Ali Safa
dc.date.accessioned2018-09-03T07:54:10Z
dc.date.available2018-09-03T07:54:10Z
dc.date.issued2014
dc.identifier.issn2193-567X
dc.identifier.doi10.1007/s13369-014-1156-x
dc.identifier.urihttp://hdl.handle.net/10072/66169
dc.description.abstractIn this paper, a modified particle swarm optimization (PSO) algorithm called autonomous groups particles swarm optimization (AGPSO) is proposed to further alleviate the two problems of trapping in local minima and slow convergence rate in solving high-dimensional problems. The main idea of AGPSO algorithm is inspired by individuals' diversity in bird flocking or insect swarming. In natural colonies, individuals are not basically quite similar in terms of intelligence and ability, but they all do their duties as members of a colony. Each individual's ability can be useful in a particular situation. In this paper, a mathematical model of diverse particles groups called autonomous groups is proposed. In other words different functions with diverse slopes, curvatures, and interception points are employed to tune the social and cognitive parameters of the PSO algorithm to give particles different behaviors as in natural colonies. The results show that PSO with autonomous groups of particles outperforms the conventional and some recent modifications of PSO in terms of escaping local minima and convergence speed. The results also indicate that dividing particles in groups and allowing them to have different individual and social thinking can improve the performance of PSO significantly.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom4683
dc.relation.ispartofpageto4697
dc.relation.ispartofissue6
dc.relation.ispartofjournalArabian Journal for Science and Engineering
dc.relation.ispartofvolume39
dc.rights.retentionY
dc.subject.fieldofresearchOptimisation
dc.subject.fieldofresearchcode010303
dc.titleAutonomous Particles Groups for Particle Swarm Optimization
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2014 Springer Berlin Heidelberg. This is an electronic version of an article published in Arabian Journal for Science and Engineering, Volume 39, Issue 6, pp 4683–4697, 2014. Arabian Journal for Science and Engineering is available online at: http://link.springer.com/ with the open URL of your article.
gro.hasfulltextFull Text
gro.griffith.authorLewis, Andrew J.
gro.griffith.authorMirjalili, Seyedali


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