Improved Differential Evolution Algorithm Based On Elite Group

被引：0
|
作者：
Gao, XiaoBo [1 ]
Wang, YouCai [1 ]
Yang, GuangZhao [1 ]
机构：
[1] High Tech Inst, Fan Gong Ting South St 12th, Qing Zhou, Shandong, Peoples R China
来源：
Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016) | 2016年 / 67卷
关键词：
DE; information entropy; average-distance-amongst-points; elite group; OPTIMIZATION;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
By introduce the information entropy and the average-distance-amongst-points to analysis the population distribution in the process of evolution, and figured out the cause of the DE/best/* premature convergence is the control function of the current optimal individual to decrease the population diversity of the algorithm. Based on the number of base vectors, improved the DE algorithm by setting up the elite group, the elite differential evolution algorithm is proposed. Finally, several typical test functions are used to test the performance. The results show that the elite differential evolution algorithm has a good performance in the search success rate and the global search capability.
引用
收藏
页码：499 / 505
页数：7
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