Enhance Differential Evolution Algorithm Based on Novel Mutation Strategy and Parameter Control Method

被引：1

作者：

Cui, Laizhong ^{[1
]}

Li, Genghui ^{[1
]}

Li, Li ^{[1
]}

Lin, Qiuzhen ^{[1
]}

Chen, Jianyong ^{[1
]}

Lu, Nan ^{[1
]}

机构：

[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Guangdong, Peoples R China

来源：

NEURAL INFORMATION PROCESSING, PT I | 2015年 / 9489卷

关键词：

Differential evolution; Mutation strategy; Parameter control method; Exploration and exploitation; OPTIMIZATION;

D O I：

10.1007/978-3-319-26532-2_70

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Differential evolution (DE) algorithm is a very effective and efficient approach for solving global numerical optimization problems. However, DE still suffers from some limitations. Moreover, the performance of DE is sensitive to its mutation strategy and associated parameters. In this paper, an enhanced differential evolution algorithm called EDE is proposed, which including a new mutation strategy and a new control method of parameters. Compared with other DE algorithms including four classical DE and two state-of-the-art DE variants on ten numerical benchmarks, the experiment results indicate that the performance of EDE is better than those of the other algorithms.

引用

页码：634 / 643

页数：10

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