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dc.contributor.authorVerma, Shanu
dc.contributor.authorPant, Millie
dc.contributor.authorSnášel, Václav
dc.date.accessioned2021-06-24T08:46:06Z
dc.date.available2021-06-24T08:46:06Z
dc.date.issued2021
dc.identifier.citationIEEE Access. 2021, vol. 9, p. 57757-57791.cs
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10084/143118
dc.description.abstractThis paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.cs
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofseriesIEEE Accesscs
dc.relation.urihttps://doi.org/10.1109/ACCESS.2021.3070634cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectNSGA-IIcs
dc.subjectcombinatorial optimizationcs
dc.subjectmulti-objective optimizationcs
dc.subjectgenetic algorithmscs
dc.titleA comprehensive review on NSGA-II for multi-objective combinatorial optimization problemscs
dc.typearticlecs
dc.identifier.doi10.1109/ACCESS.2021.3070634
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume9cs
dc.description.lastpage57791cs
dc.description.firstpage57757cs
dc.identifier.wos000641942300001


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