BEPO: A novel binary emperor penguin optimizer for automatic feature selection

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dc.authoridKaur, Amandeep/0000-0002-9825-4951
dc.authoridSingh, Krishna Kant/0000-0002-6510-6768
dc.authoridS, Vimal/0000-0002-1467-1206
dc.authoridSharma, Ashutosh/0000-0002-4990-5252
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridDhiman, Gaurav/0000-0002-6343-5197
dc.authorwosidKaur, Amandeep/IYJ-2622-2023
dc.authorwosidSingh, Krishna Kant/V-3003-2019
dc.authorwosidS, Vimal/E-9551-2016
dc.authorwosidSharma, Ashutosh/AAA-4601-2021
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidCengiz, Korhan/ABD-5559-2020
dc.authorwosidDhiman, Gaurav/AAP-6925-2020
dc.contributor.authorDhiman, Gaurav
dc.contributor.authorOliva, Diego
dc.contributor.authorKaur, Amandeep
dc.contributor.authorSingh, Krishna Kant
dc.contributor.authorVimal, S.
dc.contributor.authorSharma, Ashutosh
dc.contributor.authorCengiz, Korhan
dc.date.accessioned2024-06-12T11:00:23Z
dc.date.available2024-06-12T11:00:23Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractEmperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin's huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.knosys.2020.106560
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85095687542en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2020.106560
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20793
dc.identifier.volume211en_US
dc.identifier.wosWOS:000600316500015en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEmperor Penguin Optimizeren_US
dc.subjectFeature Selectionen_US
dc.subjectDiscrete Optimizationen_US
dc.subjectBio-Inspired Algorithmen_US
dc.subjectSpotted Hyena Optimizeren_US
dc.subjectAlgorithmen_US
dc.subjectQualityen_US
dc.titleBEPO: A novel binary emperor penguin optimizer for automatic feature selectionen_US
dc.typeArticleen_US

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