MOSOA: A new multi-objective seagull optimization algorithm

Basit Öğe Kaydı
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridDhiman, Gaurav/0000-0002-6343-5197
dc.authoridKaur, Amandeep/0000-0002-9825-4951
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridSingh, Krishna Kant/0000-0002-6510-6768
dc.authoridHoussein, Essam Halim/0000-0002-8127-7233
dc.authoridNagar, Atulya/0000-0001-5549-6435
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidDhiman, Gaurav/AAP-6925-2020
dc.authorwosidDehghani, Mohammad/JBS-3281-2023
dc.authorwosidKaur, Amandeep/IYJ-2622-2023
dc.authorwosidCengiz, Korhan/ABD-5559-2020
dc.authorwosidSingh, Krishna Kant/V-3003-2019
dc.authorwosidHoussein, Essam Halim/C-8941-2016
dc.contributor.authorDhiman, Gaurav
dc.contributor.authorSingh, Krishna Kant
dc.contributor.authorSoni, Mukesh
dc.contributor.authorNagar, Atulya
dc.contributor.authorDehghani, Mohammad
dc.contributor.authorSlowik, Adam
dc.contributor.authorKaur, Amandeep
dc.date.accessioned2024-06-12T10:58:27Z
dc.date.available2024-06-12T10:58:27Z
dc.date.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThis study introduces the extension of currently developed Seagull Optimization Algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull Optimization Algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence.en_US
dc.identifier.doi10.1016/j.eswa.2020.114150
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85097172836en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2020.114150
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20073
dc.identifier.volume167en_US
dc.identifier.wosWOS:000640531100019en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvergenceen_US
dc.subjectDiversityen_US
dc.subjectPareto Solutionsen_US
dc.subjectMulti-Objective Optimizationen_US
dc.subjectSeagull Optimization Algorithmen_US
dc.subjectEngineering Design Problemsen_US
dc.titleMOSOA: A new multi-objective seagull optimization algorithmen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu