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dc.contributor.authorScriven, Ianen_US
dc.contributor.authorIreland, Daviden_US
dc.contributor.authorLewis, Andrewen_US
dc.contributor.authorMostaghim, Sanazen_US
dc.contributor.authorBranke, Juergenen_US
dc.contributor.editorMichalewicz and Reynoldsen_US
dc.date.accessioned2017-04-24T09:52:50Z
dc.date.available2017-04-24T09:52:50Z
dc.date.issued2008en_US
dc.date.modified2011-05-04T09:52:20Z
dc.identifier.refurihttp://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4630767&isYear=2008en_AU
dc.identifier.doi10.1109/CEC.2008.4631130en_AU
dc.identifier.urihttp://hdl.handle.net/10072/22902
dc.description.abstractThis paper examines the performance characteristics of both asynchronous and synchronous parallel particle swarm optimisation algorithms in heterogeneous, fault-prone environments. Algorithm convergence is measured as a function of both iterations completed and time elapsed, allowing the two particle update mechanisms to be comprehensively evaluated and compared in such an environment. Asynchronous particle updates are shown to negatively impact the convergence speed in regards to iterations completed, however the increased parallel efficiency of the asynchronous model appears to counter this performance reduction, ensuring the asynchronous update mechanism performs comparably to the synchronous mechanism in fault-free environments. When faults are introduced, the synchronous update method is shown to suffer significant performance drops, suggesting that at least partly asynchronous algorithms should be used in real-world environments where faults can regularly occur.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent179752 bytes
dc.format.extent20682 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.titleAsynchronous Multiple Objective Particle Swarm Optimisation in Unreliable Distributed Environmentsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_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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