Show simple item record

dc.contributor.authorCai, Shaowei
dc.contributor.authorSu, Kaile
dc.contributor.authorLuo, Chuan
dc.contributor.authorSattar, Abdul
dc.date.accessioned2018-03-28T01:30:33Z
dc.date.available2018-03-28T01:30:33Z
dc.date.issued2013
dc.date.modified2014-06-11T03:12:20Z
dc.identifier.issn1076-9757
dc.identifier.doi10.1613/jair.3907
dc.identifier.urihttp://hdl.handle.net/10072/55591
dc.description.abstractThe Minimum Vertex Cover (MVC) problem is a prominent NP-hard combinatorial optimization problem of great importance in both theory and application. Local search has proved successful for this problem. However, there are two main drawbacks in state-of-the-art MVC local search algorithms. First, they select a pair of vertices to exchange simultaneously, which is timeconsuming. Secondly, although using edge weighting techniques to diversify the search, these algorithms lack mechanisms for decreasing the weights. To address these issues, we propose two new strategies: two-stage exchange and edge weighting with forgetting. The two-stage exchange strategy selects two vertices to exchange separately and performs the exchange in two stages. The strategy of edge weighting with forgetting not only increases weights of uncovered edges, but also decreases some weights for each edge periodically. These two strategies are used in designing a new MVC local search algorithm, which is referred to as NuMVC. We conduct extensive experimental studies on the standard benchmarks, namely DIMACS and BHOSLIB. The experiment comparing NuMVC with state-of-the-art heuristic algorithms show that NuMVC is at least competitive with the nearest competitor namely PLS on the DIMACS benchmark, and clearly dominates all competitors on the BHOSLIB benchmark. Also, experimental results indicate that NuMVC finds an optimal solution much faster than the current best exact algorithm for Maximum Clique on random instances as well as some structured ones. Moreover, we study the effectiveness of the two strategies and the run-time behaviour through experimental analysis.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent424583 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherAAAI Press
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofpagefrom687
dc.relation.ispartofpageto716
dc.relation.ispartofjournalJournal of Artificial Intelligence Research
dc.relation.ispartofvolume46
dc.rights.retentionY
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchComputer vision and multimedia computation
dc.subject.fieldofresearchMachine learning
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode460299
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4603
dc.subject.fieldofresearchcode4611
dc.titleNuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2013 A I Access Foundation, Inc. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorSattar, Abdul
gro.griffith.authorSu, Kaile


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record