DMFEA-II: An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems

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dc.contributor.authorOsaba, Eneko
dc.contributor.authorMartinez, Aritz D.
dc.contributor.authorGalvez, Akemi
dc.contributor.authorIglesias, Andres
dc.contributor.authorSer, Javier Del
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T11:49:54Z
dc.date.available2024-07-24T11:49:54Z
dc.date.issued2020-07-08
dc.descriptionPublisher Copyright: © 2020 ACM.
dc.description.abstractThe emerging research paradigm coined as multitasking optimization aims to solve multiple optimization tasks concurrently by means of a single search process. For this purpose, the exploitation of complementarities among the tasks to be solved is crucial, which is often achieved via the transfer of genetic material, thereby forging the Transfer Optimization field. In this context, Evolutionary Multitasking addresses this paradigm by resorting to concepts from Evolutionary Computation. Within this specific branch, approaches such as the Multifactorial Evolutionary Algorithm (MFEA) has lately gained a notable momentum when tackling multiple optimization tasks. This work contributes to this trend by proposing the first adaptation of the recently introduced Multifactorial Evolutionary Algorithm II (MFEA-II) to permutation-based discrete optimization environments. For modeling this adaptation, some concepts cannot be directly applied to discrete search spaces, such as parent-centric interactions. In this paper we entirely reformulate such concepts, making them suited to deal with permutation-based search spaces without loosing the inherent benefits of MFEA-II. The performance of the proposed solver has been assessed over 5 different multitasking setups, composed by 8 datasets of the well-known Traveling Salesman (TSP) and Capacitated Vehicle Routing Problems (CVRP). The obtained results and their comparison to those by the discrete version of the MFEA confirm the good performance of the developed dMFEA-II, and concur with the insights drawn in previous studies for continuous optimization.en
dc.description.sponsorshipEneko Osaba, Aritz D. Martinez and Javier Del Ser are supported by the Basque Government through the EMAITEK and ELKARTEK funding programs. Javier Del Ser receives support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the same institution. Andres Iglesias and Akemi Galvez thank the Computer Science National Program of the Spanish Research Agency and European Funds, Project #TIN2017-89275-R (AEI/FEDER, UE), and the PDE-GIR project of the European UnionâĂŹs Horizon 2020 programme, Marie Sklodowska-Curie Actions grant agreement #778035.
dc.description.statusPeer reviewed
dc.format.extent7
dc.identifier.citationOsaba , E , Martinez , A D , Galvez , A , Iglesias , A & Ser , J D 2020 , DMFEA-II : An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problems . in GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion . GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion , Association for Computing Machinery, Inc , pp. 1690-1696 , 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 , Cancun , Mexico , 8/07/20 . https://doi.org/10.1145/3377929.3398084
dc.identifier.citationconference
dc.identifier.doi10.1145/3377929.3398084
dc.identifier.isbn9781450371278
dc.identifier.urihttps://hdl.handle.net/11556/1922
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85089745396&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherAssociation for Computing Machinery, Inc
dc.relation.ispartofGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
dc.relation.ispartofseriesGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
dc.relation.projectIDAEI/FEDER
dc.relation.projectIDDepartment of Education
dc.relation.projectIDEuropean Funds, 2017-89275-R
dc.relation.projectIDSpanish Research Agency
dc.relation.projectIDHorizon 2020 Framework Programme, H2020
dc.relation.projectIDH2020 Marie Skłodowska-Curie Actions, MSCA, 778035
dc.relation.projectIDEusko Jaurlaritza, IT1294-19
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsDiscrete optimization
dc.subject.keywordsEvolutionary multitasking
dc.subject.keywordsMultifactorial optimization
dc.subject.keywordsTransfer optimization
dc.subject.keywordsTraveling salesman problem
dc.subject.keywordsComputational Mathematics
dc.titleDMFEA-II: An adaptive multifactorial evolutionary algorithm for permutation-based discrete optimization problemsen
dc.typeconference output
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