File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Adaptive chemical reaction optimization for global numerical optimization

TitleAdaptive chemical reaction optimization for global numerical optimization
Authors
Yu, JJLam, AYSLi, VOK
KeywordsChemical Reaction Optimization
Continuous optimization
Adaptive scheme
Metaheuristic
Evolutionary algorithm
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
Citation
The 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Sendai, Japan, 25-28 May 2015. In Conference Proceedings, 2015, p. 3192-3199 How to Cite?
DOI: http://dx.doi.org/10.1109/CEC.2015.7257288
AbstractA newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains. To alleviate the effort in tuning parameters, this paper reduces the number of optimization parameters in canonical CRO and develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO) adapts better to different optimization problems. We perform simulations with ACRO on a widely-used benchmark of continuous problems. The simulation results show that ACRO has superior performance over canonical CRO.
Persistent Identifierhttp://hdl.handle.net/10722/218961
ISBN
978-1-4799-7492-4

 

DC FieldValueLanguage
dc.contributor.authorYu, JJ-
dc.contributor.authorLam, AYS-
dc.contributor.authorLi, VOK-
dc.date.accessioned2015-09-18T07:02:17Z-
dc.date.available2015-09-18T07:02:17Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE Congress on Evolutionary Computation (CEC 2015), Sendai, Japan, 25-28 May 2015. In Conference Proceedings, 2015, p. 3192-3199-
dc.identifier.isbn978-1-4799-7492-4-
dc.identifier.urihttp://hdl.handle.net/10722/218961-
dc.description.abstractA newly proposed chemical-reaction-inspired metaheurisic, Chemical Reaction Optimization (CRO), has been applied to many optimization problems in both discrete and continuous domains. To alleviate the effort in tuning parameters, this paper reduces the number of optimization parameters in canonical CRO and develops an adaptive scheme to evolve them. Our proposed Adaptive CRO (ACRO) adapts better to different optimization problems. We perform simulations with ACRO on a widely-used benchmark of continuous problems. The simulation results show that ACRO has superior performance over canonical CRO.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284-
dc.relation.ispartofCongress on Evolutionary Computation (CEC)-
dc.rightsCongress on Evolutionary Computation (CEC). Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectChemical Reaction Optimization-
dc.subjectContinuous optimization-
dc.subjectAdaptive scheme-
dc.subjectMetaheuristic-
dc.subjectEvolutionary algorithm-
dc.titleAdaptive chemical reaction optimization for global numerical optimization-
dc.typeConference_Paper-
dc.identifier.emailYu, JJ: jqyu@eee.hku.hk-
dc.identifier.emailLam, AYS: ayslam@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.authorityLam, AYS=rp02083-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1109/CEC.2015.7257288-
dc.identifier.scopuseid_2-s2.0-84963600747-
dc.identifier.hkuros254231-
dc.identifier.hkuros254295-
dc.identifier.spage3192-
dc.identifier.epage3199-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 151119-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats