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Article: Chemical-reaction-inspired metaheuristic for optimization
Title | Chemical-reaction-inspired metaheuristic for optimization | ||||
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Authors | |||||
Keywords | Chemical reaction Metaheuristics Natureinspired algorithms Optimization methods | ||||
Issue Date | 2010 | ||||
Publisher | IEEE. | ||||
Citation | Ieee Transactions On Evolutionary Computation, 2010, v. 14 n. 3, p. 381-399 How to Cite? | ||||
Abstract | We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches. © 2006 IEEE. | ||||
Persistent Identifier | http://hdl.handle.net/10722/130634 | ||||
ISSN | 2023 Impact Factor: 11.7 2023 SCImago Journal Rankings: 5.209 | ||||
ISI Accession Number ID |
Funding Information: Manuscript received August 8, 2008; revised February 23, 2009, June 4, 2009, and September 3, 2009. First version published December 15, 2009; current version published May 28, 2010. This work was supported in part by the Strategic Research Theme of Information Technology of The University of Hong Kong. | ||||
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lam, AYS | en_HK |
dc.contributor.author | Li, VOK | en_HK |
dc.date.accessioned | 2010-12-29T03:18:40Z | - |
dc.date.available | 2010-12-29T03:18:40Z | - |
dc.date.issued | 2010 | en_HK |
dc.identifier.citation | Ieee Transactions On Evolutionary Computation, 2010, v. 14 n. 3, p. 381-399 | en_HK |
dc.identifier.issn | 1089-778X | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/130634 | - |
dc.description.abstract | We encounter optimization problems in our daily lives and in various research domains. Some of them are so hard that we can, at best, approximate the best solutions with (meta-) heuristic methods. However, the huge number of optimization problems and the small number of generally acknowledged methods mean that more metaheuristics are needed to fill the gap. We propose a new metaheuristic, called chemical reaction optimization (CRO), to solve optimization problems. It mimics the interactions of molecules in a chemical reaction to reach a low energy stable state. We tested the performance of CRO with three nondeterministic polynomial-time hard combinatorial optimization problems. Two of them were traditional benchmark problems and the other was a real-world problem. Simulation results showed that CRO is very competitive with the few existing successful metaheuristics, having outperformed them in some cases, and CRO achieved the best performance in the real-world problem. Moreover, with the No-Free-Lunch theorem, CRO must have equal performance as the others on average, but it can outperform all other metaheuristics when matched to the right problem type. Therefore, it provides a new approach for solving optimization problems. CRO may potentially solve those problems which may not be solvable with the few generally acknowledged approaches. © 2006 IEEE. | en_HK |
dc.language | eng | - |
dc.publisher | IEEE. | - |
dc.relation.ispartof | IEEE Transactions on Evolutionary Computation | en_HK |
dc.rights | ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
dc.subject | Chemical reaction | en_HK |
dc.subject | Metaheuristics | en_HK |
dc.subject | Natureinspired algorithms | en_HK |
dc.subject | Optimization methods | en_HK |
dc.title | Chemical-reaction-inspired metaheuristic for optimization | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1089-778X&volume=14&issue=3&spage=381&epage=399&date=2010&atitle=Chemical-reaction-inspired+metaheuristic+for+optimization | - |
dc.identifier.email | Li, VOK:vli@eee.hku.hk | en_HK |
dc.identifier.authority | Li, VOK=rp00150 | en_HK |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/TEVC.2009.2033580 | en_HK |
dc.identifier.scopus | eid_2-s2.0-77953082526 | en_HK |
dc.identifier.hkuros | 181381 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-77953082526&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 14 | en_HK |
dc.identifier.issue | 3 | en_HK |
dc.identifier.spage | 381 | en_HK |
dc.identifier.epage | 399 | en_HK |
dc.identifier.isi | WOS:000278277000004 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Lam, AYS=35322184700 | en_HK |
dc.identifier.scopusauthorid | Li, VOK=7202621685 | en_HK |
dc.identifier.citeulike | 7412987 | - |
dc.identifier.issnl | 1089-778X | - |