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Article: Real-coded chemical reaction optimization
Title | Real-coded chemical reaction optimization | ||||||
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Authors | |||||||
Keywords | Chemical Reaction Optimization Continuous Optimization Metaheuristics | ||||||
Issue Date | 2012 | ||||||
Citation | IEEE Transactions on Evolutionary Computation, 2012, v. 16 n. 3, p. 339-353 How to Cite? | ||||||
Abstract | Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain. © 2012 IEEE. | ||||||
Persistent Identifier | http://hdl.handle.net/10722/155765 | ||||||
ISSN | 2023 Impact Factor: 11.7 2023 SCImago Journal Rankings: 5.209 | ||||||
ISI Accession Number ID |
Funding Information: This work was supported in part by the Strategic Research Theme of Information Technology of the University of Hong Kong. The work of A. Y. S. Lam was also supported in part by the Croucher Foundation Research Fellowship. | ||||||
References |
DC Field | Value | Language |
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dc.contributor.author | Lam, AYS | en_US |
dc.contributor.author | Li, VOK | en_US |
dc.contributor.author | Yu, JJQ | en_US |
dc.date.accessioned | 2012-08-08T08:35:14Z | - |
dc.date.available | 2012-08-08T08:35:14Z | - |
dc.date.issued | 2012 | en_US |
dc.identifier.citation | IEEE Transactions on Evolutionary Computation, 2012, v. 16 n. 3, p. 339-353 | en_US |
dc.identifier.issn | 1089-778X | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/155765 | - |
dc.description.abstract | Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain. © 2012 IEEE. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | IEEE Transactions on Evolutionary Computation | en_US |
dc.subject | Chemical Reaction Optimization | en_US |
dc.subject | Continuous Optimization | en_US |
dc.subject | Metaheuristics | en_US |
dc.title | Real-coded chemical reaction optimization | en_US |
dc.type | Article | en_US |
dc.identifier.email | Li, VOK:vli@eee.hku.hk | en_US |
dc.identifier.authority | Li, VOK=rp00150 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1109/TEVC.2011.2161091 | en_US |
dc.identifier.scopus | eid_2-s2.0-84861817748 | en_US |
dc.identifier.hkuros | 210451 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-84861817748&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 16 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.spage | 339 | en_US |
dc.identifier.epage | 353 | en_US |
dc.identifier.isi | WOS:000304823400003 | - |
dc.publisher.place | United States | en_US |
dc.identifier.scopusauthorid | Lam, AYS=35322184700 | en_US |
dc.identifier.scopusauthorid | Li, VOK=7202621685 | en_US |
dc.identifier.scopusauthorid | Yu, JJQ=51865147800 | en_US |
dc.identifier.issnl | 1089-778X | - |