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Article: On the convergence of chemical reaction optimization for combinatorial optimization

TitleOn the convergence of chemical reaction optimization for combinatorial optimization
Authors
KeywordsConvergence rate
Finite absorbing Markov chain
First hitting time
Chemical reaction optimization (CRO)
Convergence
Issue Date2013
Citation
IEEE Transactions on Evolutionary Computation, 2013, v. 17, n. 5, p. 605-620 How to Cite?
AbstractA novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-world problems. However, all studies concerning CRO have been empirical in nature and no theoretical analysis has been conducted to study its convergence properties. In this paper, we present some convergence results for several generic versions of CRO, each of which adopts different combinations of elementary reactions. We investigate the limiting behavior of CRO. By modeling CRO as a finite absorbing Markov chain, we show that CRO converges to a global optimum solution with a probability arbitrarily close to one when time tends to infinity. Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO. © 1997-2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/219720
ISSN
2015 Impact Factor: 5.908
2015 SCImago Journal Rankings: 4.308

 

DC FieldValueLanguage
dc.contributor.authorLam, Albert Y S-
dc.contributor.authorLi, Victor O K-
dc.contributor.authorXu, Jin-
dc.date.accessioned2015-09-23T02:57:48Z-
dc.date.available2015-09-23T02:57:48Z-
dc.date.issued2013-
dc.identifier.citationIEEE Transactions on Evolutionary Computation, 2013, v. 17, n. 5, p. 605-620-
dc.identifier.issn1089-778X-
dc.identifier.urihttp://hdl.handle.net/10722/219720-
dc.description.abstractA novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-world problems. However, all studies concerning CRO have been empirical in nature and no theoretical analysis has been conducted to study its convergence properties. In this paper, we present some convergence results for several generic versions of CRO, each of which adopts different combinations of elementary reactions. We investigate the limiting behavior of CRO. By modeling CRO as a finite absorbing Markov chain, we show that CRO converges to a global optimum solution with a probability arbitrarily close to one when time tends to infinity. Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO. © 1997-2012 IEEE.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Evolutionary Computation-
dc.rightsIEEE Transactions on Evolutionary Computation. Copyright © IEEE-
dc.rights©2012 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectConvergence rate-
dc.subjectFinite absorbing Markov chain-
dc.subjectFirst hitting time-
dc.subjectChemical reaction optimization (CRO)-
dc.subjectConvergence-
dc.titleOn the convergence of chemical reaction optimization for combinatorial optimization-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TEVC.2012.2227973-
dc.identifier.scopuseid_2-s2.0-84885104636-
dc.identifier.hkuros225401-
dc.identifier.volume17-
dc.identifier.issue5-
dc.identifier.spage605-
dc.identifier.epage620-

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