A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm

Basit Öğe Kaydı
dc.contributor.authorOzkis, Ahmet
dc.contributor.authorBabalik, Ahmet
dc.date.accessioned2020-03-26T19:33:35Z
dc.date.available2020-03-26T19:33:35Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis study investigates a multi-objective Vortex Search algorithm (MOVS) by modifying the single-objective Vortex Search algorithm or VS. The VS is a metaheuristic-based algorithm that uses a new adaptive step-size adjustment strategy to improve the performance of the search process. Search mechanism of the VS is inspired by the vortex pattern, so it is called a "Vortex Search" algorithm. The original VS is an improved way of solving single-objective continuous problems. To improve the MOVS algorithm, the VS algorithm is enhanced with added calculation approaches, such as fast-nondominated-sorting and crowding-distance, in order to identify the degree of non-dominance of the solutions and the densities of their occurrence. In addition, a crossover operation is added to the MOVS algorithm in order to enhance the Pareto front convergence capacity of the solutions. Finally, to spread the solutions more successfully over the Pareto front, it has been randomly produced using the inverse incomplete gamma function using a parameter between 0 and 1. The proposed MOVS algorithm is tested against 36 different benchmark problems together with NSGAII, MOCeII, IBEA and MOEA/D algorithms. The test results indicate that the MOVS algorithm achieves a better performance on accuracy and convergence speed than any other algorithms when comparisons are made against several test problems, and they also show that it is a competitive algorithm. (C) 2017 Elsevier Inc. All rights reserved.en_US
dc.description.sponsorshipSelcuk University Academic Staff Training Program Coordination Unit (OYP Program) [2014-OYP-059]en_US
dc.description.sponsorshipThe authors thank Selcuk University Academic Staff Training Program Coordination Unit (OYP Program, 2014-OYP-059) for their institutional support. The authors also wish to thank Dr. Tahir Sag for his precious helps at the beginning of the study.en_US
dc.identifier.doi10.1016/j.ins.2017.03.026en_US
dc.identifier.endpage148en_US
dc.identifier.issn0020-0255en_US
dc.identifier.issn1872-6291en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage124en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.ins.2017.03.026
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34745
dc.identifier.volume402en_US
dc.identifier.wosWOS:000400726300009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.ispartofINFORMATION SCIENCESen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectVortex search algorithmen_US
dc.subjectMulti-objective optimizationen_US
dc.subjectMetaheuristicsen_US
dc.subjectNon-dominated sorting genetic algorithm-IIen_US
dc.titleA novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithmen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu