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dc.contributor.authorLewis, Andrewen_US
dc.contributor.authorIreland, Daviden_US
dc.contributor.editorMichalewicz and Reynoldsen_US
dc.date.accessioned2017-05-03T12:48:57Z
dc.date.available2017-05-03T12:48:57Z
dc.date.issued2008en_US
dc.date.modified2011-05-04T09:49:51Z
dc.identifier.refurihttp://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4630767&isYear=2008en_AU
dc.identifier.doi10.1109/CEC.2008.4631086en_AU
dc.identifier.urihttp://hdl.handle.net/10072/22903
dc.description.abstractThis paper proposes an approach to the solution of multi-objective optimisation problems that delivers a single, preferred solution. A conventional, population-based, multiobjective optimisation method is used to provide a set of solutions approximating the Pareto front. As the set of solutions evolves, an approximation to the Pareto front is derived using a Kriging method. This approximate surface is traversed using a single objective optimisation method, driven by a simple, aggregated objective function that expresses design preferences. The approach is demonstrated using a combination of multi-objective particle swarm optimisation (MOPSO) and the Simplex method of Nelder and Mead, applied to several, standard, multi-objective test problems. Good, compromise solutions meeting user-defined design preferences are delivered without manual intervention.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent6296889 bytes
dc.format.extent28816 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherOnlineen_US
dc.publisher.placeOnlineen_US
dc.publisher.urihttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4625778en_AU
dc.relation.ispartofstudentpublicationNen_AU
dc.relation.ispartofconferencename2008 IEEE World Congress on Computational Intelligenceen_US
dc.relation.ispartofconferencetitleIEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence).en_US
dc.relation.ispartofdatefrom2008-06-01en_US
dc.relation.ispartofdateto2008-06-06en_US
dc.relation.ispartoflocationHong Kong, Chinaen_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode230118en_US
dc.titleAutomated Solution Selection in 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 2008 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.en_AU
gro.date.issued2008
gro.hasfulltextFull Text


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

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