Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/96604
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Liang, J | en_US |
dc.creator | Ban, X | en_US |
dc.creator | Yu, K | en_US |
dc.creator | Qu, B | en_US |
dc.creator | Qiao, K | en_US |
dc.creator | Yue, C | en_US |
dc.creator | Chen, K | en_US |
dc.creator | Tan, KC | en_US |
dc.date.accessioned | 2022-12-07T02:55:35Z | - |
dc.date.available | 2022-12-07T02:55:35Z | - |
dc.identifier.issn | 1089-778X | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/96604 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | The following publication J. Liang et al., "A Survey on Evolutionary Constrained Multiobjective Optimization," in IEEE Transactions on Evolutionary Computation, vol. 27, no. 2, pp. 201-221, April 2023 is available at https://doi.org/10.1109/TEVC.2022.3155533. | en_US |
dc.subject | Constrained multi-objective optimization | en_US |
dc.subject | Evolutionary algorithms | en_US |
dc.subject | Constraint handling | en_US |
dc.subject | Benchmark test problems | en_US |
dc.title | A survey on evolutionary constrained multi-objective optimization | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 201 | en_US |
dc.identifier.epage | 221 | en_US |
dc.identifier.volume | 27 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1109/TEVC.2022.3155533 | en_US |
dcterms.abstract | Handling constrained multi-objective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multi-objective optimization. We first review a large number of CMOEAs through categorization, and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multi-objective optimization. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on evolutionary computation, Apr. 2023, v. 27, no. 3, p. 201-221 | en_US |
dcterms.isPartOf | IEEE transactions on evolutionary computation | en_US |
dcterms.issued | 2023-04 | - |
dc.identifier.scopus | 2-s2.0-85125729429 | - |
dc.identifier.eissn | 1941-0026 | en_US |
dc.description.validate | 202212 bckw | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Liang_Survey_Evolutionary_Constrained.pdf | 2.08 MB | Adobe PDF | View/Open |
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