Real-Coded Chemical Reaction Optimization

被引:122
作者
Lam, Albert Y. S. [1 ]
Li, Victor O. K. [2 ,3 ]
Yu, James J. Q. [2 ]
机构
[1] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA
[2] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam, Hong Kong, Peoples R China
[3] King Saud Univ, Dept Comp Engn, Riyadh 11451, Saudi Arabia
关键词
Chemical reaction optimization; continuous optimization; metaheuristics; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHMS; MACHINES; COLONY;
D O I
10.1109/TEVC.2011.2161091
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Optimization problems can generally be classified as continuous and discrete, based on the nature of the solution space. A recently developed chemical-reaction-inspired metaheuristic, called chemical reaction optimization (CRO), has been shown to perform well in many optimization problems in the discrete domain. This paper is dedicated to proposing a real-coded version of CRO, namely, RCCRO, to solve continuous optimization problems. We compare the performance of RCCRO with a large number of optimization techniques on a large set of standard continuous benchmark functions. We find that RCCRO outperforms all the others on the average. We also propose an adaptive scheme for RCCRO which can improve the performance effectively. This shows that CRO is suitable for solving problems in the continuous domain.
引用
收藏
页码:339 / 353
页数:15
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