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dc.contributor.authorPullan, Wayneen_US
dc.contributor.authorMascia, Francoen_US
dc.contributor.authorBrunato, Mauroen_US
dc.date.accessioned2017-05-03T12:40:32Z
dc.date.available2017-05-03T12:40:32Z
dc.date.issued2011en_US
dc.date.modified2013-05-29T08:30:50Z
dc.identifier.issn13811231en_US
dc.identifier.doi10.1007/s10732-010-9131-5en_US
dc.identifier.urihttp://hdl.handle.net/10072/37609
dc.description.abstractThe advent of desktop multi-core computers has dramatically improved the usability of parallel algorithms which, in the past, have required specialised hardware. This paper introduces cooperating local search (CLS), a parallelised hyper-heuristic for the maximum clique problem. CLS utilises cooperating low level heuristics which alternate between sequences of iterative improvement, during which suitable vertices are added to the current clique, and plateau search, where vertices of the current clique are swapped with vertices not in the current clique. These low level heuristics differ primarily in their vertex selection techniques and their approach to dealing with plateaus. To improve the performance of CLS, guidance information is passed between low level heuristics directing them to particular areas of the search domain. In addition, CLS dynamically reconfigures the allocation of low level heuristics to cores, based on information obtained during a trial, to ensure that the mix of low level heuristics is appropriate for the instance being optimised. CLS has no problem instance dependent parameters, improves the state-of-the-art performance for the maximum clique problem over all the BHOSLIB benchmark instances and attains unprecedented consistency over the state-of-the-art on the DIMACS benchmark instances.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherSpringer New York LLCen_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom181en_US
dc.relation.ispartofpageto199en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalJournal of Heuristicsen_US
dc.relation.ispartofvolume17en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classifieden_US
dc.subject.fieldofresearchcode080199en_US
dc.titleCooperating local search for the maximum clique problemen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


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