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Real-Coded Chemical Reaction Optimization

delete2012-06-01
delete133
PRE
AI
A
Albert Y. S. Lam *
V
Victor O. K. Li
J
James J. Q. Yu
DOI:10.1109/TEVC.2011.2161091delete
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Abstract

Abstract

En 中文
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.
Keywords:
Chemical reaction optimization
continuous optimization
metaheuristics

Journal

IEEE Transactions on Evolutionary Computation cover
IEEE Transactions on Evolutionary Computation
IF:
12
Papers:
1.9K
Citations:
2.4W

Organization

U
University of California Berkeley
Scholars:
3.5W
Papers: 2.8W
Citations: 11.3W
University of California System cover
University of California System
Scholars:
37.7W
Papers: 33.8W
Citations: 6.6K
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