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dc.contributor.authorLewis, Andrew
dc.contributor.editorAndy Tyrrell
dc.date.accessioned2017-05-03T12:49:02Z
dc.date.available2017-05-03T12:49:02Z
dc.date.issued2009
dc.date.modified2010-07-28T06:58:32Z
dc.identifier.isbn978-1-4244-2958-5
dc.identifier.refurihttp://www.cec-2009.org/
dc.identifier.doi10.1109/CEC.2009.4983302
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent1388024 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placePiscataway, NJ, USA
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameIEEE Congress on Evolutionary Computation
dc.relation.ispartofconferencetitle2009 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-5
dc.relation.ispartofdatefrom2009-05-18
dc.relation.ispartofdateto2009-05-21
dc.relation.ispartoflocationTrondheim, NORWAY
dc.relation.ispartofpagefrom2866
dc.relation.ispartofpageto2870
dc.rights.retentionY
dc.subject.fieldofresearchOptimisation
dc.subject.fieldofresearchcode490304
dc.titleLoCost: a Spatial Social Network Algorithm for Multi-Objective Optimisation
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 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.
gro.date.issued2009
gro.hasfulltextFull Text
gro.griffith.authorLewis, Andrew J.


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

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