dc.contributor.author | Li, K | |
dc.contributor.author | Chen, R | |
dc.contributor.author | Fu, G | |
dc.contributor.author | Yao, X | |
dc.date.accessioned | 2019-03-15T09:52:52Z | |
dc.date.issued | 2018-07-18 | |
dc.description.abstract | When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multi-objective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multi-objective optimizers. | en_GB |
dc.description.sponsorship | Royal Society (Government) | en_GB |
dc.description.sponsorship | Ministry of Science and Technology of China | en_GB |
dc.description.sponsorship | Science and Technology Innovation Committee Foundation of Shenzhen | en_GB |
dc.description.sponsorship | Shenzhen Peacock Plan | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Published online 19 July 2018 | en_GB |
dc.identifier.doi | 10.1109/TEVC.2018.2855411 | |
dc.identifier.grantnumber | IEC\NSFC\170243 | en_GB |
dc.identifier.grantnumber | 2017YFC0804003 | en_GB |
dc.identifier.grantnumber | ZDSYS201703031748284 | en_GB |
dc.identifier.grantnumber | KQTD2016112514355531 | en_GB |
dc.identifier.grantnumber | EP/J017515/1 | en_GB |
dc.identifier.grantnumber | EP/P005578/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/36480 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | Published under a CC-BY licence. | en_GB |
dc.subject | Multi-objective optimization | en_GB |
dc.subject | constraint handling | en_GB |
dc.subject | evolutionary algorithm | en_GB |
dc.subject | two archive strategy | en_GB |
dc.title | Two-archive evolutionary algorithm for constrained multi objective optimization | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-03-15T09:52:52Z | |
dc.identifier.issn | 1089-778X | |
dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record | en_GB |
dc.identifier.journal | IEEE Transactions on Evolutionary Computation | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2018-07-18 | |
exeter.funder | ::Royal Society (Government) | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2018-07-18 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-03-15T09:04:24Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-03-15T09:52:55Z | |
refterms.panel | B | en_GB |
refterms.depositException | publishedGoldOA | |
refterms.depositExceptionExplanation | https://doi.org/10.1109/tevc.2018.2855411 | |