Publication:
A hybrid optimizer based on backtracking search and differential evolution for continuous optimization

dc.contributor.authorKuyu, Yiğit Cağatay
dc.contributor.authorOnieva, Enrique
dc.contributor.authorLopez-Garcia, Pedro
dc.contributor.buuauthorKUYU, YİĞİT ÇAĞATAY
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentElektrik Elektronik Mühendisliği Bölümü
dc.contributor.orcid0000-0002-7054-3102
dc.contributor.researcheridAAC-6923-2021
dc.date.accessioned2024-06-06T12:15:20Z
dc.date.available2024-06-06T12:15:20Z
dc.date.issued2021-01-02
dc.description.abstractThis paper introduces a novel hybridisation technique combining the Backtracking Search (BS) and Differential Evolution (DE) algorithms. The proposed hybridisation executes diversity loss and stagnation detection mechanisms to maintain the diversity of the populations, in addition, modifications are done over the mutation operators of the component algorithms in order to improve the search capability of the proposal. These modifications are self-adapted and implemented simultaneously. Extensive experiments to establish the optimal configuration of the parameters are also presented through the introduced technique. The proposed hybridisation approach has been applied to five classical versions and two state-of-the-art variants of DE and tested against 28 well-known benchmark functions with different dimensions, each type of which highlights a different set of characteristics and provides a baseline measurement to validate the performance of the algorithms. In order to further test the proposal, the four outstanding algorithms in the state of the art have also been included in the comparisons. Experimental results show the effectiveness of the proposed hybrid framework over the compared algorithms.
dc.identifier.doi10.1080/0952813X.2021.1872109
dc.identifier.eissn1362-3079
dc.identifier.endpage385
dc.identifier.issn0952-813X
dc.identifier.issue3
dc.identifier.startpage355
dc.identifier.urihttps://doi.org/10.1080/0952813X.2021.1872109
dc.identifier.urihttps://www.tandfonline.com/doi/full/10.1080/0952813X.2021.1872109
dc.identifier.urihttps://hdl.handle.net/11452/41845
dc.identifier.volume34
dc.identifier.wos000609614700001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherTaylor & Francis
dc.relation.journalJournal of Experimental & Theoretical Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectContinuous optimisation
dc.subjectHybrid algorithm
dc.subjectDifferential evolution
dc.subjectBacktracking search
dc.subjectParameter setting
dc.subjectComputer science
dc.titleA hybrid optimizer based on backtracking search and differential evolution for continuous optimization
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Elektrik Elektronik Mühendisliği Bölümü
relation.isAuthorOfPublication04fc60e2-d4a3-4614-b912-4d7d5e1ab573
relation.isAuthorOfPublication.latestForDiscovery04fc60e2-d4a3-4614-b912-4d7d5e1ab573

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