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dc.contributor.authorLewis, Andrewen_US
dc.contributor.authorMostaghim, Sanazen_US
dc.contributor.authorScriven, Ianen_US
dc.contributor.editorA. Lewis, S. Mostaghim and M. Randallen_US
dc.date.accessioned2017-04-24T09:52:58Z
dc.date.available2017-04-24T09:52:58Z
dc.date.issued2009en_US
dc.date.modified2013-03-25T22:12:30Z
dc.identifier.isbn9783642012617en_US
dc.identifier.doi10.1007/978-3-642-01262-4_3en_US
dc.identifier.urihttp://hdl.handle.net/10072/29269
dc.description.abstractThis chapter examines the performance characteristics of both asynchronous and synchronous parallel particle swarm optimisation algorithms in heterogeneous, fault-prone environments. The chapter starts with a simple parallelisation paradigm, the Master-Slave model using Multi-Objective Particle Swarm Optimisation (MOPSO) in a heterogeneous environment. Extending the investigation to general, distributed environments, algorithm convergence is measured as a function of both iterations completed and time elapsed. Asynchronous particle updates are shown to perform comparably to synchronous updates 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. Finally, the issue of how to utilise newly available nodes, as well as the loss of existing nodes, is considered and two methods of generating new particles during algorithm execution are investigated.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent571283 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherSpringer-Verlagen_US
dc.publisher.placeBerlinen_US
dc.publisher.urihttp://www.springerlink.com/en_US
dc.relation.ispartofbooktitleBiologically-inspired Optimisation Methods: Parallel Algorithms, Systems and Applicationsen_US
dc.relation.ispartofchapter3en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom51en_US
dc.relation.ispartofpageto78en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchOptimisationen_US
dc.subject.fieldofresearchDistributed and Grid Systemsen_US
dc.subject.fieldofresearchcode010303en_US
dc.subject.fieldofresearchcode080501en_US
dc.titleAsynchronous Multi-objective Optimisation in Unreliable Distributed Environmentsen_US
dc.typeBook chapteren_US
dc.type.descriptionB1 - Book Chapters (HERDC)en_US
dc.type.codeB - Book Chaptersen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.rights.copyrightCopyright 2009 Springer. This is the author-manuscript version of this paper. It is reproduced here in accordance with the copyright policy of the publisher. Please refer to the publisher’s website for further information.en_US
gro.date.issued2009
gro.hasfulltextFull Text


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