Learning to guide particle search for dynamic multi-objective optimization

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dc.contributor.authorSong, Wei
dc.contributor.authorLiu, Shaocong
dc.contributor.authorWang, Xinjie
dc.contributor.authorYang, Shengxiang
dc.contributor.authorJin, Yaochu
dc.date.acceptance2024-02-03
dc.date.accessioned2024-03-05T13:38:55Z
dc.date.available2024-03-05T13:38:55Z
dc.date.issued2024-02-23
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 multiobjective optimization problems (DMOPs) are characterized by multiple objectives that change over time in varying environments. More specifically, environmental changes can be described as various dynamics. However, it is difficult for existing dynamic multiobjective algorithms (DMOAs) to handle DMOPs due to their inability to learn in different environments to guide the search. Besides, solving DMOPs is typically an online task, requiring low computational cost of a DMOA. To address the above challenges, we propose a particle search guidance network (PSGN), capable of directing individuals’ search actions, including learning target selection and acceleration coefficient control. PSGN can learn the actions that should be taken in each environment through rewarding or punishing the network by reinforcement learning. Thus, PSGN is capable of tackling DMOPs of various dynamics. Additionally, we efficiently adjust PSGN hidden nodes and update the output weights in an incremental learning way, enabling PSGN to direct particle search at a low computational cost. We compare the proposed PSGN with seven state-of-the-art algorithms, and the excellent performance of PSGN verifies that it can handle DMOPs of various dynamics in a computationally very efficient way.
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., Wang, X., Yang, S. and Jin, Y. (2024) Learning to guide particle search for dynamic multi-objective optimization. IEEE Transactions on Cybernetics,
dc.identifier.doihttps://doi.org/10.1109/TCYB.2024.3364375
dc.identifier.issn2168-2275
dc.identifier.urihttps://hdl.handle.net/2086/23621
dc.language.isoen
dc.peerreviewedYes
dc.projectid62076110
dc.projectidBK20181341
dc.publisherIEEE
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.subjectDynamic multiobjective optimization
dc.subjectincremental learning
dc.subjectneural network
dc.subjectparticle swarm optimization
dc.subjectreinforcement learning
dc.titleLearning to guide particle search for dynamic multi-objective optimization
dc.typeArticle

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