Combining state detection with knowledge transfer for constrained multi-objective optimization

Simple item page
dc.contributor.authorZheng, Jinhua
dc.contributor.authorYang, Kaixi
dc.contributor.authorZou, Juan
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2022-09
dc.date.accessioned2023-10-25T13:31:24Z
dc.date.available2023-10-25T13:31:24Z
dc.date.issued0202-04-18
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.abstractThe main challenge in studying constrained multi-objective optimization problems (CMOPs) is reasonably balancing convergence, diversity, and feasibility. One of the most successful solutions to this challenge is the co-evolutionary frame-work, an algorithm in which multiple populations cooperate and complement each other, with different but interdependent populations addressing different but related problems. However, the effectiveness of existing algorithms for information exchange between various populations is not apparent. This paper proposes a new algorithm named SDKT using population state detection and knowledge transfer. The method has dual stages (i.e., knowledge acquisition and knowledge reception) and dual populations. Specifically, by restarting the strategy, these two populations (i.e., mainPop and auxPop) first explore more feasible regions with and without constraints. Then, in the knowledge receiving stage, ma i nPop and auxPop provide effective information to promote each other's approach to constrained PF (CPF) and unconstrained PF (UPF), respectively. Extensive experiments on three well-known test suites and three real-world problem studies fully demonstrate that SDKT is more competitive than five state-of-the-art constrained multi-objective evolutionary algorithms.
dc.exception.reasonNot deposited within three months of publication
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.citationJ. Zheng, K. Yang, J. Zou, and S. Yang. (2023) Combining state detection with knowledge transfer for constrained multi-objective optimization. Proceedings of the IEEE 34th International Conference on Tools with Artificial Intelligence, pp. 712-719
dc.identifier.doihttps://doi.org/10.1109/ICTAI56018.2022.00110
dc.identifier.isbn9798350397444
dc.identifier.issn2375-0197
dc.identifier.urihttps://hdl.handle.net/2086/23299
dc.language.isoen
dc.peerreviewedYes
dc.projectid62176228, 61876164
dc.projectid2020JJ4590
dc.publisherIEEE
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.subjectConstrained multi-objective optimization problems
dc.subjectco-evolutionary framework
dc.subjectknowledge acquisition
dc.subjectknowledge reception
dc.titleCombining state detection with knowledge transfer for constrained multi-objective optimization
dc.typeConference

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ICTAI22.pdf
Size:
1.95 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
4.2 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

School of Computer Science and Informatics