An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem

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dc.authoridOzcan, Ender/0000-0003-0276-1391
dc.authoridSonuc, Emrullah/0000-0001-7425-6963
dc.contributor.authorSonuc, Emrullah
dc.contributor.authorOzcan, Ender
dc.date.accessioned2024-09-29T15:57:10Z
dc.date.available2024-09-29T15:57:10Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractMetaheuristics, providing high level guidelines for heuristic optimisation, have successfully been applied to many complex problems over the past decades. However, their performances often vary depending on the choice of the initial settings for their parameters and operators along with the characteristics of the given problem instance handled. Hence, there is a growing interest into designing adaptive search methods that automate the selection of efficient operators and setting of their parameters during the search process. In this study, an adaptive binary parallel evolutionary algorithm, referred to as ABPEA, is introduced for solving the uncapacitated facility location problem which is proven to be an NP-hard optimisation problem. The approach uses a unary and two other binary operators. A reinforcement learning mechanism is used for assigning credits to operators considering their recent impact on generating improved solutions to the problem instance in hand. An operator is selected adaptively with a greedy policy for perturbing a solution. The performance of the proposed approach is evaluated on a set of well-known benchmark instances using ORLib and M*, and its scaling capacity by running it with different starting points on an increasing number of threads. Parameters are adjusted to derive the best configuration of three different rewarding schemes, which are instant, average and extreme. A performance comparison to the other state-of-the-art algorithms illustrates the superiority of ABPEA. Moreover, ABPEA provides up to a factor of 3.9 times acceleration when compared to the sequential algorithm based on a single-operator.en_US
dc.description.sponsorshipScientific and Technological Research Council of Tuerkiye (TUEBITAK) [1059B192001306]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Tuerkiye (TUEBITAK) under the BIDEB-2219 International Postdoctoral Research Fellowship Programme grant number1059B192001306.en_US
dc.identifier.doi10.1016/j.eswa.2023.119956
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85151430467en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2023.119956
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4631
dc.identifier.volume224en_US
dc.identifier.wosWOS:000970025000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAdaptive operator selectionen_US
dc.subjectMetaheuristicsen_US
dc.subjectCombinatorial optimisationen_US
dc.subjectParallel algorithmsen_US
dc.titleAn adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problemen_US
dc.typeArticleen_US

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