AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

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dc.contributor.authorOsaba, Eneko
dc.contributor.authorDel Ser, Javier
dc.contributor.authorMartinez, Aritz D.
dc.contributor.authorLobo, Jesus L.
dc.contributor.authorHerrera, Francisco
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.accessioned2024-07-24T12:00:42Z
dc.date.available2024-07-24T12:00:42Z
dc.date.issued2021-09
dc.descriptionPublisher Copyright: © 2021 Elsevier Inc.
dc.description.abstractTransfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process.en
dc.description.sponsorshipThe authors thank the Basque Government for its support through the Consolidated Research Group MATHMODE (IT1294-19), as well as the ELKARTEK program (3KIA project, Ref. KK-2020/00049). Authors also acknowledge the financial support from the project SCOTT: Secure Connected Trustable Things (ECSEL Joint Undertaking, Ref. 737422). Francisco Herrera would like to thank the Spanish Government for its funding support (SMART-DaSCI project, TIN2017-89517-P). The authors thank the Basque Government for its support through the Consolidated Research Group MATHMODE (IT1294-19), as well as the ELKARTEK program (3KIA project, Ref. KK-2020/00049). Authors also acknowledge the financial support from the project SCOTT: Secure Connected Trustable Things (ECSEL Joint Undertaking, Ref. 737422). Francisco Herrera would like to thank the Spanish Government for its funding support (SMART-DaSCI project, TIN2017-89517-P).
dc.description.statusPeer reviewed
dc.format.extent22
dc.identifier.citationOsaba , E , Del Ser , J , Martinez , A D , Lobo , J L & Herrera , F 2021 , ' AT-MFCGA : An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking ' , Information Sciences , vol. 570 , pp. 577-598 . https://doi.org/10.1016/j.ins.2021.05.005
dc.identifier.doi10.1016/j.ins.2021.05.005
dc.identifier.issn0020-0255
dc.identifier.urihttps://hdl.handle.net/11556/3068
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85107657283&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofInformation Sciences
dc.relation.projectIDSpanish Government, TIN2017-89517-P
dc.relation.projectIDEusko Jaurlaritza, 737422
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsEvolutionary multitask optimization
dc.subject.keywordsMultifactorial evolutionary algorithm
dc.subject.keywordsMultitasking
dc.subject.keywordsTransfer optimization
dc.subject.keywordsTheoretical Computer Science
dc.subject.keywordsSoftware
dc.subject.keywordsControl and Systems Engineering
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsInformation Systems and Management
dc.subject.keywordsArtificial Intelligence
dc.titleAT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitaskingen
dc.typejournal article
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