dc.contributor.author | Wu, M | |
dc.contributor.author | Li, K | |
dc.contributor.author | Kwong, S | |
dc.contributor.author | Zhang, Q | |
dc.contributor.author | Zhang, J | |
dc.date.accessioned | 2019-07-12T12:37:03Z | |
dc.date.issued | 2018-08-17 | |
dc.description.abstract | The decomposition-based evolutionary multiobjective optimization (EMO) algorithm has become an increasingly popular choice for a posteriori multiobjective optimization. However, recent studies have shown that their performance strongly depends on the Pareto front (PF) shapes. This can be attributed to the decomposition method, of which the reference points and subproblem formulation settings are not well adaptable to various problem characteristics. In this paper, we develop a learning-to-decompose (LTD) paradigm that adaptively sets the decomposition method by learning the characteristics of the estimated PF. Specifically, it consists of two interdependent parts, i.e., a learning module and an optimization module. Given the current nondominated solutions from the optimization module, the learning module periodically learns an analytical model of the estimated PF. Thereafter, useful information is extracted from the learned model to set the decomposition method for the optimization module: 1) reference points compliant with the PF shape and 2) subproblem formulations whose contours and search directions are appropriate for the current status. Accordingly, the optimization module, which can be any decomposition-based EMO algorithm in principle, decomposes the multiobjective optimization problem into a number of subproblems and optimizes them simultaneously. To validate our proposed LTD paradigm, we integrate it with two decomposition-based EMO algorithms, and compare them with four state-of-the-art algorithms on a series of benchmark problems with various PF shapes. | en_GB |
dc.description.sponsorship | Royal Society | en_GB |
dc.identifier.citation | Vol. 23 (3), pp. 376 - 390 | en_GB |
dc.identifier.doi | 10.1109/TEVC.2018.2865931 | |
dc.identifier.uri | http://hdl.handle.net/10871/37967 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_GB |
dc.title | Learning to decompose: a paradigm for decomposition-based multiobjective optimization | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-07-12T12:37:03Z | |
dc.identifier.issn | 1089-778X | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record | en_GB |
dc.identifier.journal | IEEE transactions on evolutionary computation | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2018-08-03 | |
exeter.funder | ::Royal Society (Government) | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2019-06-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-07-12T12:28:26Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-08-01T14:23:09Z | |
refterms.panel | B | en_GB |