An adaptive trade-off evolutionary algorithm with composite differential evolution for constrained multi-objective optimization

dc.contributor.authorFeng, Jian
dc.contributor.authorLiu, Shaoning
dc.contributor.authorYang, Shengxiang
dc.contributor.authorZheng, Jun
dc.contributor.authorLiu, Jinze
dc.date.acceptance2023-08-17
dc.date.accessioned2024-01-24T13:52:09Z
dc.date.available2024-01-24T13:52:09Z
dc.date.issued2023-08-19
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.description.abstractConvergence, diversity, and feasibility are crucial factors in solving constrained multi-objective optimization problems (CMOPs). Their imbalance can result in the algorithm failing to converge well to the Pareto front, especially when dealing with complex CMOPs. To address this issue, we propose an adaptive tradeoff evolutionary algorithm (ATEA), which can adjust the environment selection strategy based on the characteristics of problem, aiming to achieve a balance between convergence and diversity while ensuring feasibility of the population. The ATEA divides the search process into three phases: In the extended exploration phase, a global search is conducted using a guided constraint relaxation technique to enable the population to quickly traverse the infeasible region and approach the feasible region. In the tradeoff exploration phase, constraints are further detected and estimated to retain more feasible individuals and competing infeasible individuals, allowing the population to accurately identify all possible feasible regions and gradually expand towards the feasible boundary. The exploitation phase explores under-explored regions in the earlier phases with the aim of accelerating the convergence of the population and escaping from the local optima. Extensive experiments conducted on four benchmark test suites demonstrate that ATEA exhibits superior performance in three benchmark test suites compared with six other state-of-the-art algorithms.
dc.exception.reasonnot deposited within three months of acceptance
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.funder.otherLiaoNing Revitalization Talents Program, China
dc.identifier.citationFeng, J., Liu, S., Yang, S., Zheng, J. and Liu, J. (2023) An adaptive trade-off evolutionary algorithm with composite differential evolution for constrained multi-objective optimization. Swarm and Evolutionary Computation, 83, 101386
dc.identifier.doihttps://doi.org/10.1016/j.swevo.2023.101386
dc.identifier.urihttps://hdl.handle.net/2086/23488
dc.language.isoen
dc.peerreviewedYes
dc.projectidU22A2055, 62173081
dc.projectidXLYC2002032
dc.publisherElsevier
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.rightsAttribution-NonCommercial-NoDerivs 2.0 UK: England & Walesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/uk/
dc.subjectAdaptive tradeoff model
dc.subjectComposite differential evolution
dc.subjectConstrained multi-objective optimization
dc.subjectConstraint handling techniques
dc.titleAn adaptive trade-off evolutionary algorithm with composite differential evolution for constrained multi-objective optimization
dc.typeArticle

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