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Article: Chemical-reaction-inspired metaheuristic for optimization

TitleChemical-reaction-inspired metaheuristic for optimization
Authors
KeywordsChemical reaction
Metaheuristics
Natureinspired algorithms
Optimization methods
Issue Date2010
PublisherIEEE.
Citation
Ieee Transactions On Evolutionary Computation, 2010, v. 14 n. 3, p. 381-399 How to Cite?
AbstractWe 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 Identifierhttp://hdl.handle.net/10722/130634
ISSN
2015 Impact Factor: 5.908
2015 SCImago Journal Rankings: 4.308
ISI Accession Number ID
Funding AgencyGrant Number
Strategic Research Theme of Information Technology of The University of Hong Kong
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 FieldValueLanguage
dc.contributor.authorLam, AYSen_HK
dc.contributor.authorLi, VOKen_HK
dc.date.accessioned2010-12-29T03:18:40Z-
dc.date.available2010-12-29T03:18:40Z-
dc.date.issued2010en_HK
dc.identifier.citationIeee Transactions On Evolutionary Computation, 2010, v. 14 n. 3, p. 381-399en_HK
dc.identifier.issn1089-778Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/130634-
dc.description.abstractWe 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.languageeng-
dc.publisherIEEE.-
dc.relation.ispartofIEEE Transactions on Evolutionary Computationen_HK
dc.rightsIEEE Transactions on Evolutionary Computation. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2010 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.subjectChemical reactionen_HK
dc.subjectMetaheuristicsen_HK
dc.subjectNatureinspired algorithmsen_HK
dc.subjectOptimization methodsen_HK
dc.titleChemical-reaction-inspired metaheuristic for optimizationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://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.emailLi, VOK:vli@eee.hku.hken_HK
dc.identifier.authorityLi, VOK=rp00150en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TEVC.2009.2033580en_HK
dc.identifier.scopuseid_2-s2.0-77953082526en_HK
dc.identifier.hkuros181381-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77953082526&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume14en_HK
dc.identifier.issue3en_HK
dc.identifier.spage381en_HK
dc.identifier.epage399en_HK
dc.identifier.isiWOS:000278277000004-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLam, AYS=35322184700en_HK
dc.identifier.scopusauthoridLi, VOK=7202621685en_HK
dc.identifier.citeulike7412987-

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