A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization

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dc.contributor.authorZou, Juan
dc.contributor.authorTang, Li
dc.contributor.authorLiu, Yuan
dc.contributor.authorYang, Shengxiang
dc.contributor.authorWang. Shiting
dc.date.acceptance2024-05-05
dc.date.accessioned2024-06-28T13:15:08Z
dc.date.available2024-06-28T13:15:08Z
dc.date.issued2024-05-17
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.abstractLarge-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources.
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.funder.otherNatural Science Foundation of Hunan Province, China
dc.identifier.citationZou, J., Tang, L., Liu, Y., Yang, S. and Wang, S. (2024) A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization. Information Sciences, 674, 120719
dc.identifier.doihttps://doi.org/10.1016/j.ins.2024.120719
dc.identifier.urihttps://hdl.handle.net/2086/23964
dc.language.isoen
dc.peerreviewedYes
dc.projectid62276224, 6230073173
dc.projectid2022JJ40452
dc.publisherElsevier
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEvolutionary algorithms
dc.subjectGlobal direction search
dc.subjectHybrid direction search
dc.subjectLarge-scale multiobjective optimization
dc.subjectLocal direction search
dc.titleA two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization
dc.typeArticle

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