Efficient hybrid memetic algorithm for multi-objective optimization problems

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dc.contributor.authorMohammed, Tareq Abed
dc.contributor.authorSahmoud, Shaaban
dc.contributor.authorBayat, Oğuz
dc.date.accessioned2021-05-15T12:36:53Z
dc.date.available2021-05-15T12:36:53Z
dc.date.issued2017
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Elektrik - Elektronik Mühendisliği Bölümüen_US
dc.descriptionInternational Conference on Engineering and Technology (ICET) -- AUG 21-23, 2017 -- Akdeniz Univ, Antalya, TURKEY
dc.description.abstractImportance of multi-objective optimization problems has been rapidly increasing in the artificial intelligence community. 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. 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 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.description.sponsorshipIARES, IEEEen_US
dc.identifier.citationMohammed, T. A., Sahmoud, S., Bayat, O. (2017). Efficient hybrid memetic algorithm for multi-objective optimization problems. In 2017 International Conference on Engineering and Technology (ICET) (1-6).
dc.identifier.isbn978-1-5386-1949-0
dc.identifier.issn2380-9345
dc.identifier.scopus2-s2.0-85047847646
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/427
dc.identifier.wosWOS:000454987100040
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorMohammed, Tareq Abed
dc.institutionauthorBayat, Oguz
dc.language.isoen
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Conference on Engineering and Technology (Icet)
dc.relation.ispartofseriesInternational Conference on Engineering and Technology
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - 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.subjectHybrid Algorithmsen_US
dc.titleEfficient hybrid memetic algorithm for multi-objective optimization problems
dc.typeConference Object

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Elektrik - Elektronik Mühendisliği Bölümü Koleksiyonu
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