Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters
Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters
复制标题
具有复合试验向量生成策略和控制参数的差分进化
DOI：
10.1109/tevc.2010.2087271
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发表时间：
2011-02-01
影响因子：
14.3
通讯作者：
Zhang, Qingfu
中科院分区：
文献类型：
--
作者：
Wang, Yong;Cai, Zixing;Zhang, Qingfu
试验矢量产生策略和控制参数对差异进化（DE）的性能有重大影响。本文研究了通过将几种有效的试验矢量生成策略与某些合适的控制参数设置相结合，是否可以改善DE的性能。本文提出了一种称为复合DE（代码）的新方法。该方法使用三个试验矢量生成策略和三个控制参数设置。它将它们随机结合在一起，以生成试验向量。代码已在所有CEC2005竞赛测试实例上进行了测试。实验结果表明，代码非常具竞争力。
Trial vector generation strategies and control parameters have a significant influence on the performance of differential evolution (DE). This paper studies whether the performance of DE can be improved by combining several effective trial vector generation strategies with some suitable control parameter settings. A novel method, called composite DE (CoDE), has been proposed in this paper. This method uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. CoDE has been tested on all the CEC2005 contest test instances. Experimental results show that CoDE is very competitive.