.png){kind=icon}
返回
Real-Coded Chemical Reaction Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2011.2161091.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Chemical reaction optimization
continuous optimization
metaheuristics
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
引用论文
Hybrid crossover operators for real-coded genetic algorithms:: an experimental study实数编码遗传算法的混合交叉算子: 实验研究.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
分享收藏
查看更多内容