Please use this identifier to cite or link to this item: https://doi.org/10.1109/TEVC.2008.920671
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dc.titleA competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization
dc.contributor.authorGoh, C.-K.
dc.contributor.authorTan, K.C.
dc.date.accessioned2014-10-07T04:22:25Z
dc.date.available2014-10-07T04:22:25Z
dc.date.issued2009
dc.identifier.citationGoh, C.-K., Tan, K.C. (2009). A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation 13 (1) : 103-127. ScholarBank@NUS Repository. https://doi.org/10.1109/TEVC.2008.920671
dc.identifier.issn1089778X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/81849
dc.description.abstractIn addition to the need for satisfying several competing objectives, many real-world applications are also dynamic and require the optimization algorithm to track the changing optimum over time. This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multiobjective optimization problems and to track the Pareto front in a dynamic environment. The main idea of competitive-cooperative coevolution is to allow the decomposition process of the optimization problem to adapt and emerge rather than being hand designed and fixed at the start of the evolutionary optimization process. In particular, each species subpopulation will compete to represent a particular subcomponent of the multiobjective problem, while the eventual winners will cooperate to evolve for better solutions. Through such an iterative process of competition and cooperation, the various subcomponents are optimized by different species subpopulations based on the optimization requirements of that particular time instant, enabling the coevolutionary algorithm to handle both the static and dynamic multiobjective problems. The effectiveness of the competitive-cooperation coevolutionary algorithm (COEA) in static environments is validated against various multiobjective evolutionary algorithms upon different benchmark problems characterized by various difficulties in local optimality, discontinuity, nonconvexity, and high-dimensionality. In addition, extensive studies are also conducted to examine the capability of dynamic COEA (dCOEA) in tracking the Pareto front as it changes with time in dynamic environments. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TEVC.2008.920671
dc.sourceScopus
dc.subjectCoevolution
dc.subjectDynamic multiobjective optimization
dc.subjectEvolutionary algorithms
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TEVC.2008.920671
dc.description.sourcetitleIEEE Transactions on Evolutionary Computation
dc.description.volume13
dc.description.issue1
dc.description.page103-127
dc.description.codenITEVF
dc.identifier.isiut000263161700008
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