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dc.contributor.authorPremkumar, M
dc.contributor.authorJangir, Pradeep
dc.contributor.authorSowmya, R
dc.contributor.authorAlhelou, Hassan Haes
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorKumar, B Santhosh
dc.date.accessioned2022-01-19T05:09:55Z
dc.date.available2022-01-19T05:09:55Z
dc.date.issued2021
dc.identifier.issn2288-5048
dc.identifier.doi10.1093/jcde/qwab065
dc.identifier.urihttp://hdl.handle.net/10072/411594
dc.description.abstractThis paper proposes a new Multi-Objective Equilibrium Optimizer (MOEO) to handle complex optimization problems, including real-world engineering design optimization problems. The Equilibrium Optimizer (EO) is a recently reported physics-based metaheuristic algorithm, and it has been inspired by the models used to predict equilibrium state and dynamic state. A similar procedure is utilized in MOEO by combining models in a different target search space. The crowding distance mechanism is employed in the MOEO algorithm to balance exploitation and exploration phases as the search progresses. In addition, a non-dominated sorting strategy is also merged with the MOEO algorithm to preserve the population diversity and it has been considered as a crucial problem in multi-objective metaheuristic algorithms. An archive with an update function is used to uphold and improve the coverage of Pareto with optimal solutions. The performance of MOEO is validated for 33 contextual problems with 6 constrained, 12 unconstrained, and 15 practical constrained engineering design problems, including non-linear problems. The result obtained by the proposed MOEO algorithm is compared with other state-of-the-art multi-objective optimization algorithms. The quantitative and qualitative results indicate that the proposed MOEO provides more competitive outcomes than the different algorithms. From the results obtained for all 33 benchmark optimization problems, the efficiency, robustness, and exploration ability to solve multi-objective problems of the MOEO algorithm are well defined and clarified. The paper is further supported with extra online service and guideline at https://premkumarmanoharan.wixsite.com/mysite.
dc.description.peerreviewedYes
dc.languageen
dc.publisherOxford University Press (OUP)
dc.relation.ispartofpagefrom24
dc.relation.ispartofpageto50
dc.relation.ispartofissue1
dc.relation.ispartofjournalJournal of Computational Design and Engineering
dc.relation.ispartofvolume9
dc.subject.fieldofresearchQuantum engineering systems (incl. computing and communications)
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode400912
dc.subject.fieldofresearchcode40
dc.subject.fieldofresearchcode46
dc.titleMulti-objective equilibrium optimizer: framework and development for solving multi-objective optimization problems
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationPremkumar, M; Jangir, P; Sowmya, R; Alhelou, HH; Mirjalili, S; Kumar, BS, Multi-objective equilibrium optimizer: framework and development for solving multi-objective optimization problems, Journal of Computational Design and Engineering, 2021, 9 (1), pp. 24-50
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2022-01-19T02:34:38Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Computational Design and Engineering. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorMirjalili, Seyedali


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