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dc.contributor.authorMostaghim, Sanazen_US
dc.contributor.authorBranke, Jurgenen_US
dc.contributor.authorLewis, Andrewen_US
dc.contributor.authorSchmeck, Hartmuten_US
dc.contributor.editorMichalewicz and Reynoldsen_US
dc.date.accessioned2017-05-03T12:48:58Z
dc.date.available2017-05-03T12:48:58Z
dc.date.issued2008en_US
dc.date.modified2011-05-04T09:53:27Z
dc.identifier.refurihttp://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4630767&isYear=2008en_AU
dc.identifier.doi10.1109/CEC.2008.4631060en_AU
dc.identifier.urihttp://hdl.handle.net/10072/22904
dc.description.abstractIn this paper, we study parallelization of multiobjective optimization algorithms on a set of hetergeneous resources based on the Master-Slave model. The Master-Slave model is known to be the simplest parallelization paradigm, where a master processor sends function evaluations to several slave processors. The critical issue when using the standard methods on heterogeneous resources is that in every iteration of the optimization, the master processor has to wait for all of the computing resources (including the slow ones) to deliver the evaluations. In this paper, we study a new algorithm where all of the available computing resources are efficiently utilized to perform the multi-objective optimization task independent of the speed (fast or slow) of the computing processors. For this we propose a hybrid method using Multi-objective Particle Swarm optimization and Binary search methods. The new algorithm has been tested on a scenario contaning heterogeneous resources and the results show that not only does the new algorithm perform well for parallel resources, but also when compared to a normal serial run on one computeren_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent291604 bytes
dc.format.extent27517 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.titleParallel Multi-objective Optimization using Master-Slave Model on Heterogeneous Resourcesen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_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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