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dc.contributor.authorChen, Qingda
dc.contributor.authorDing, Jinliang
dc.contributor.authorYang, Shengxiang
dc.contributor.authorChai, Tianyou
dc.date.accessioned2019-12-03T15:28:50Z
dc.date.available2019-12-03T15:28:50Z
dc.date.issued2019-12
dc.identifier.citationChen, Q., Ding, J., Yang, S., and Chai, T. (2019) A novel evolutionary algorithm for dynamic constrained multiobjective optimization problems. IEEE Transactions on Evolutionary Computation, in press.en
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/18905
dc.descriptionThe file attached to this record is the author's final peer reviewed version.en
dc.description.abstractTo promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity–concavity and connectedness–disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. Experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectDynamic constrained multiobjective optimizationen
dc.subjecttest problemsen
dc.subjectpopulation selectionen
dc.subjectchange responseen
dc.titleA novel evolutionary algorithm for dynamic constrained multiobjective optimization problemsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1109/tevc.2019.2958075
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectidGrant 61988101, Grant 61525302, Grant 61590922, and Grant 61673331en
dc.cclicenceN/Aen
dc.date.acceptance2019-11
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherNational Natural Science Foundation of Chinaen


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