Improved differential evolution with local search

被引：0

作者：

机构：

[1] College of Information Engineering, North China University of Technology

[2] Department of Information Engineering, Hunan Urban Construction College

来源：

Li, H. (yudianhl@163.com) | 1600年 / Advanced Institute of Convergence Information Technology卷 / 07期

关键词：

Differential evolution; Evolutionary computation; Global optimization; Opposition-based learning;

D O I：

10.4156/jcit.vol7.issue4.24

中图分类号：

学科分类号：

摘要：

Differential evolution (DE) is a popular meta-heuristic optimizer which has shown good performance in solving many real-life and benchmark optimization problems. However, DE usually shows slow convergence rate at the last stage of the evolution. To enhance the performance of DE, this paper proposed an improved DE variant (OLSDE) which employs opposition-based concept and local search strategy. Experimental studies on several benchmark functions demonstrate that our approach outperforms standard DE and other two improved DE algorithms.

引用

页码：197 / 204

页数：7

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