File Download
 
Links for fulltext
(May Require Subscription)
 
Supplementary

Conference Paper: Real-coded chemical reaction optimization with different perturbation functions
  • Basic View
  • Metadata View
  • XML View
TitleReal-coded chemical reaction optimization with different perturbation functions
 
AuthorsYu, JJQ1
Lam, AYS2
Li, VOK1
 
KeywordsChemical reaction optimization
Gaussian distribution
Cauchy distribution
Exponential distribution
Rayleigh distribution
Evolutionary algorithm
 
Issue Date2012
 
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
 
CitationThe 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings. 2012, p. 1-8 [How to Cite?]
 
AbstractChemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of wellknown benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of optimization problems. © 2012 IEEE.
 
DescriptionIEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)
 
ISBN978-1-4673-1509-8
 
DC FieldValue
dc.contributor.authorYu, JJQ
 
dc.contributor.authorLam, AYS
 
dc.contributor.authorLi, VOK
 
dc.date.accessioned2012-09-20T08:16:51Z
 
dc.date.available2012-09-20T08:16:51Z
 
dc.date.issued2012
 
dc.description.abstractChemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of wellknown benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of optimization problems. © 2012 IEEE.
 
dc.description.naturepublished_or_final_version
 
dc.descriptionIEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)
 
dc.identifier.citationThe 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings. 2012, p. 1-8 [How to Cite?]
 
dc.identifier.epage8
 
dc.identifier.hkuros210466
 
dc.identifier.isbn978-1-4673-1509-8
 
dc.identifier.scopuseid_2-s2.0-84866875012
 
dc.identifier.spage1
 
dc.identifier.urihttp://hdl.handle.net/10722/165305
 
dc.languageeng
 
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284
 
dc.publisher.placeUnited States
 
dc.relation.ispartofCongress on Evolutionary Computation Proceedings
 
dc.rightsCongress on Evolutionary Computation Proceedings. 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.subjectChemical reaction optimization
 
dc.subjectGaussian distribution
 
dc.subjectCauchy distribution
 
dc.subjectExponential distribution
 
dc.subjectRayleigh distribution
 
dc.subjectEvolutionary algorithm
 
dc.titleReal-coded chemical reaction optimization with different perturbation functions
 
dc.typeConference_Paper
 
<?xml encoding="utf-8" version="1.0"?>
<item><contributor.author>Yu, JJQ</contributor.author>
<contributor.author>Lam, AYS</contributor.author>
<contributor.author>Li, VOK</contributor.author>
<date.accessioned>2012-09-20T08:16:51Z</date.accessioned>
<date.available>2012-09-20T08:16:51Z</date.available>
<date.issued>2012</date.issued>
<identifier.citation>The 2012 IEEE Congress on Evolutionary Computation (CEC 2012), Brisbane, Australia, 10-15 June 2012. In IEEE CEC Proceedings. 2012, p. 1-8</identifier.citation>
<identifier.isbn>978-1-4673-1509-8</identifier.isbn>
<identifier.uri>http://hdl.handle.net/10722/165305</identifier.uri>
<description>IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012  hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)</description>
<description.abstract>Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of wellknown benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of optimization problems. &#169; 2012 IEEE.</description.abstract>
<language>eng</language>
<publisher>IEEE. The Journal&apos;s web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000284</publisher>
<relation.ispartof>Congress on Evolutionary Computation Proceedings</relation.ispartof>
<rights>Congress on Evolutionary Computation Proceedings. Copyright &#169; IEEE.</rights>
<rights>&#169;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.</rights>
<rights>Creative Commons: Attribution 3.0 Hong Kong License</rights>
<subject>Chemical reaction optimization</subject>
<subject>Gaussian distribution</subject>
<subject>Cauchy distribution</subject>
<subject>Exponential distribution</subject>
<subject>Rayleigh distribution</subject>
<subject>Evolutionary algorithm</subject>
<title>Real-coded chemical reaction optimization with different perturbation functions</title>
<type>Conference_Paper</type>
<description.nature>published_or_final_version</description.nature>
<identifier.scopus>eid_2-s2.0-84866875012</identifier.scopus>
<identifier.hkuros>210466</identifier.hkuros>
<identifier.spage>1</identifier.spage>
<identifier.epage>8</identifier.epage>
<publisher.place>United States</publisher.place>
<bitstream.url>http://hub.hku.hk/bitstream/10722/165305/1/Content.pdf</bitstream.url>
</item>
Author Affiliations
  1. The University of Hong Kong
  2. UC Berkeley