A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphs

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dc.contributor.authorOsabay, Eneko
dc.contributor.authorVillar-Rodriguezy, Esther
dc.contributor.authorSeryz, Javier Del
dc.contributor.institutionQuantum
dc.date.accessioned2024-07-24T11:54:38Z
dc.date.available2024-07-24T11:54:38Z
dc.date.issued2020-12-01
dc.descriptionPublisher Copyright: © 2020 IEEE.
dc.description.abstractThe main goal of the multitasking optimization paradigm is to solve multiple and concurrent optimization tasks in a simultaneous way through a single search process. For attaining promising results, potential complementarities and synergies between tasks are properly exploited, helping each other by virtue of the exchange of genetic material. This paper is focused on Evolutionary Multitasking, which is a perspective for dealing with multitasking optimization scenarios by embracing concepts from Evolutionary Computation. This work contributes to this field by presenting a new multitasking approach named as Coevolutionary Variable Neighborhood Search Algorithm, which finds its inspiration on both the Variable Neighborhood Search metaheuristic and coevolutionary strategies. The second contribution of this paper is the application field, which is the optimal partitioning of graph instances whose connections among nodes are directed and weighted. This paper pioneers on the simultaneous solving of this kind of tasks. Two different multitasking scenarios are considered, each comprising 11 graph instances. Results obtained by our method are compared to those issued by a parallel Variable Neighborhood Search and independent executions of the basic Variable Neighborhood Search. The discussion on such results support our hypothesis that the proposed method is a promising scheme for simultaneous solving community detection problems over graphs.en
dc.description.statusPeer reviewed
dc.format.extent7
dc.identifier.citationOsabay , E , Villar-Rodriguezy , E & Seryz , J D 2020 , A Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS) : Application to Community Detection over Graphs . in 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 . , 9308447 , 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 , Institute of Electrical and Electronics Engineers Inc. , pp. 768-774 , 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 , Virtual, Canberra , Australia , 1/12/20 . https://doi.org/10.1109/SSCI47803.2020.9308447
dc.identifier.citationconference
dc.identifier.doi10.1109/SSCI47803.2020.9308447
dc.identifier.isbn9781728125473
dc.identifier.urihttps://hdl.handle.net/11556/2424
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85099688374&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
dc.relation.ispartofseries2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsCommunity Detection
dc.subject.keywordsEvolutionary Multitasking
dc.subject.keywordsTransfer Optimization
dc.subject.keywordsVariable Neighborhood Search
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsDecision Sciences (miscellaneous)
dc.titleA Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking (CoVNS): Application to Community Detection over Graphsen
dc.typeconference output
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