Efficient hybrid multi-objective evolutionary algorithm

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dc.contributor.authorMohammed, Tareq Abed
dc.contributor.authorBayat, Oğuz
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2021-05-15T12:41:54Z
dc.date.available2021-05-15T12:41:54Z
dc.date.issued2018
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIn the artificial intelligence community the multi-objective optimization problem become very common and has been rapidly increasing attention. This significant is due to the fact that there is high number of real-world applications having optimization problems that include more than one objective function. As has been evident in the last ten years, the evolutionary algorithms are one of the best choices to solve multi-objective optimization problems. Although evolutionary algorithms are the most common approach to solve multi-objective optimization problems, there is still many issues and drawbacks that need solving and enhancing. In this paper a set of improved hybrid Memetic evolutionary algorithms are proposed to solve multi-objective optimization problems. The proposed algorithms enhance the performance of NSGA-II algorithm by using different new proposed and simple search schemes. Merging a simple and efficient search technique to NSGA-II significantly enhances the convergence ability and speed of the algorithm. To assess the performance of proposed algorithms, three multi-objective test problems are used from ZDT set. Our empirical results in this paper show that the proposed algorithms significantly enhance the NSGA-II algorithm performance in both diversity and convergence.en_US
dc.identifier.endpage26en_US
dc.identifier.issn1738-7906
dc.identifier.issue3en_US
dc.identifier.startpage19en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/869
dc.identifier.volume18en_US
dc.identifier.wosWOS:000432494400004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.institutionauthorMohammed, Tareq Abed
dc.language.isoen
dc.publisherInt Journal Computer Science & Network Security-Ijcsnsen_US
dc.relation.ispartofInternational Journal of Computer Science and Network Security
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEvolutionary Algorithmsen_US
dc.subjectMemetic Algorithmsen_US
dc.subjectMulti-Objective Optimizationen_US
dc.subjectHigh Dimensional Problemsen_US
dc.subjectHybrid Algorithmsen_US
dc.titleEfficient hybrid multi-objective evolutionary algorithm
dc.typeArticle

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