Multi-region trend prediction strategy with online sequential extreme learning machine for dynamic multi-objective optimization

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dc.contributor.authorSong, Wei
dc.contributor.authorLiu, Shaocong
dc.contributor.authorYu, Hongbin
dc.contributor.authorGuo, Yinan
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2024-08
dc.date.accessioned2024-08-19T16:19:18Z
dc.date.available2024-08-19T16:19:18Z
dc.date.issued2024-08-07
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.abstractDynamic multi-objective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, requiring dynamic multi-objective algorithms (DMOAs) to track changing Pareto-optimal fronts. In recent decade, prediction-based DMOAs have shown promise in handling DMOPs. However, in existing prediction-based DMOAs some specific solutions in a small number of prior environments are generally used. Consequently, it is difficult for these DMOAs to capture Pareto-optimal set (POS) changes accurately. Besides, gaps may exist in some objective subspaces due to uneven population distribution, causing a difficulty in searching these subspaces. Faced with such difficulties, this article proposes a multi-region trend prediction strategy-based dynamic multi-objective evolutionary algorithm (MTPS-DMOEA) to handle DMOPs. MTPS-DMOEA divides the objective space into multiple subspaces and predicts POS moving trends through the use of POS center points from multiple objective subspaces, which contributes to accurately capturing POS changes. In MTPS-DMOEA, the parameters of the prediction model are continuously updated via online sequential extreme learning machine, facilitating the adequate utilization of useful information in historical environments and hence the enhancement of the generalization performance for the prediction. To fill gaps in some objective subspaces, MTPS-DMOEA introduces diverse solutions generated from the previous POS in adjacent subspaces. We compare the proposed MTPS-DMOEA with six state-of-the-art DMOAs on fourteen benchmark test problems, and the experimental results demonstrate the excellent performance of MTPS-DMOEA in handling DMOPs.
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.funder.otherNatural Science Foundation of Jiangsu Province, China
dc.identifier.citationSong, W., Liu, S., Yu, H., Guo, Y. and Yang, S. (2024) Multi-region trend prediction strategy with online sequential extreme learning machine for dynamic multi-objective optimization. IEEE Transactions on Emerging Topics in Computational Intelligence,
dc.identifier.doihttps://doi.org/10.1109/TETCI.2024.3437166
dc.identifier.urihttps://hdl.handle.net/2086/24131
dc.language.isoen
dc.peerreviewedYes
dc.projectid62076110
dc.projectidBK20181341
dc.publisherIEEE
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 computation
dc.subjectprediction model
dc.subjectonline sequential learning
dc.subjectextreme learning machine
dc.subjectdynamic multi-objective optimization
dc.titleMulti-region trend prediction strategy with online sequential extreme learning machine for dynamic multi-objective optimization
dc.typeArticle

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