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dc.contributor.authorPullan, W
dc.contributor.authorMascia, F
dc.contributor.authorBrunato, M
dc.date.accessioned2017-05-03T12:40:32Z
dc.date.available2017-05-03T12:40:32Z
dc.date.issued2011
dc.date.modified2013-05-29T08:30:50Z
dc.identifier.issn1381-1231
dc.identifier.doi10.1007/s10732-010-9131-5
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.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer New York LLC
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom181
dc.relation.ispartofpageto199
dc.relation.ispartofissue2
dc.relation.ispartofjournalJournal of Heuristics
dc.relation.ispartofvolume17
dc.rights.retentionY
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode460299
dc.subject.fieldofresearchcode4613
dc.titleCooperating local search for the maximum clique problem
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.date.issued2011
gro.hasfulltextNo Full Text
gro.griffith.authorPullan, Wayne J.


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