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dc.contributor.authorLewis, Andrewen_US
dc.contributor.editorAndy Tyrrellen_US
dc.date.accessioned2017-04-24T09:52:56Z
dc.date.available2017-04-24T09:52:56Z
dc.date.issued2009en_US
dc.date.modified2010-07-28T06:58:32Z
dc.identifier.refurihttp://www.cec-2009.org/en_AU
dc.identifier.doi10.1109/CEC.2009.4983302en_AU
dc.identifier.urihttp://hdl.handle.net/10072/29237
dc.description.abstractParticle Swarm Optimisation (PSO) is increasingly being applied to optimisation of problems in engineering design and scientific investigation. While readily adapted to singleobjective problems, its use on multi-objective problems is hampered by the difficulty of finding effective means of guiding the swarm in the presence of multiple, competing objectives. This paper suggests a novel approach to this problem, based on an extension of the concepts of spatial social networks using a model of the behaviour of locusts and crickets. Comparison is made between neighbouring particles based on Pareto dominance, and a corresponding repulsion between particles added to previously suggested attractive forces. Computational experiments demonstrate that the new, spatial, social network optimisation algorithm can provide results comparable to a conventional MOPSO algorithm, and improved coverage of the Pareto-front.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent1388024 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.publisher.placePiscataway, NJ, USAen_US
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename(CEC 2009) IEEE Congress on Evolutionary Computationen_US
dc.relation.ispartofconferencetitleConference Proceedings: IEEE Congress on Evolutionary Computation 2009en_US
dc.relation.ispartofdatefrom2009-05-18en_US
dc.relation.ispartofdateto2009-05-21en_US
dc.relation.ispartoflocationTrondheim, Norwayen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchOptimisationen_US
dc.subject.fieldofresearchcode010303en_US
dc.titleLoCost: a Spatial Social Network Algorithm for Multi-Objective Optimisationen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This paper was first published in the Proceedings of IEEE Congress on Evolutionary Computation, 2009.en_AU
gro.date.issued2009
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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