A hybrid SCA inspired BBO for feature selection problems

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dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0001-7466-0368en_US
dc.authorid0000-0002-3527-8825en_US
dc.contributor.authorSindhu, Ru
dc.contributor.authorNgadiran, Ruzelita
dc.contributor.authorYacob, Yasmin Mohd
dc.contributor.authorZahri, Nik Adilah Hanin
dc.contributor.authorHariharan, Muthusamy
dc.contributor.authorPolat, Kemal
dc.date.accessioned2021-06-23T19:52:42Z
dc.date.available2021-06-23T19:52:42Z
dc.date.issued2019
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractRecent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.en_US
dc.identifier.doi10.1155/2019/9517568
dc.identifier.issn1024-123X
dc.identifier.issn1563-5147
dc.identifier.scopus2-s2.0-85065804829en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1155/2019/9517568
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10195
dc.identifier.volume2019en_US
dc.identifier.wosWOS:000464822300001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofMathematical Problems In Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHybrid SCAen_US
dc.titleA hybrid SCA inspired BBO for feature selection problemsen_US
dc.typeArticleen_US

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