Adaptive Chemical Reaction Optimization for global numerical optimization

James J.Q. Yu; Yun Sang Albert LAM; Victor O.K. Li

doi:10.1109/CEC.2015.7257288

Adaptive Chemical Reaction Optimization for global numerical optimization

James J.Q. Yu, Yun Sang Albert LAM, Victor O.K. Li

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

A 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.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	IEEE
Pages	3192-3199
Number of pages	8
ISBN (Electronic)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257288
Publication status	Published - 10 Sept 2015
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015

Publication series

Name	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

Conference	IEEE Congress on Evolutionary Computation, CEC 2015
Country/Territory	Japan
City	Sendai
Period	25/05/15 → 28/05/15

Scopus Subject Areas

Computer Science Applications
Computational Mathematics

User-Defined Keywords

adaptive scheme
Chemical Reaction Optimization
continuous optimization
evolutionary algorithm
metaheuristic

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10.1109/CEC.2015.7257288

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title = "Adaptive Chemical Reaction Optimization for global numerical optimization",

abstract = "A 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.",

keywords = "adaptive scheme, Chemical Reaction Optimization, continuous optimization, evolutionary algorithm, metaheuristic",

author = "Yu, {James J.Q.} and LAM, {Yun Sang Albert} and Li, {Victor O.K.}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; IEEE Congress on Evolutionary Computation, CEC 2015 ; Conference date: 25-05-2015 Through 28-05-2015",

year = "2015",

month = sep,

day = "10",

doi = "10.1109/CEC.2015.7257288",

language = "English",

series = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

publisher = "IEEE",

pages = "3192--3199",

booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

address = "United States",

}

Yu, JJQ, LAM, YSA & Li, VOK 2015, Adaptive Chemical Reaction Optimization for global numerical optimization. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257288, 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings, IEEE, pp. 3192-3199, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 25/05/15. https://doi.org/10.1109/CEC.2015.7257288

Adaptive Chemical Reaction Optimization for global numerical optimization. / Yu, James J.Q.; LAM, Yun Sang Albert; Li, Victor O.K.

2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. IEEE, 2015. p. 3192-3199 7257288 (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings).

Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review

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T1 - Adaptive Chemical Reaction Optimization for global numerical optimization

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AU - LAM, Yun Sang Albert

AU - Li, Victor O.K.

PY - 2015/9/10

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N2 - A 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.

AB - A 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.

KW - adaptive scheme

KW - Chemical Reaction Optimization

KW - continuous optimization

KW - evolutionary algorithm

KW - metaheuristic

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M3 - Conference contribution

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BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

PB - IEEE

T2 - IEEE Congress on Evolutionary Computation, CEC 2015

Y2 - 25 May 2015 through 28 May 2015

ER -

Adaptive Chemical Reaction Optimization for global numerical optimization

Abstract

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User-Defined Keywords

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