A self-adaptive differential evolution algorithm for continuous optimization problems

被引：6

作者：

Jitkongchuen D. ^{[1
]}

Thammano A. ^{[1
]}

机构：

[1] Computational Intelligence Laboratory, King Mongkut’s Institute of Technology Ladkrabang, Bangkok

来源：

Artificial Life and Robotics | 2014年 / 19卷 / 02期

关键词：

Differential evolution; Meta-heuristic algorithm; Optimization; Self-adaptive system;

D O I：

10.1007/s10015-014-0155-z

中图分类号：

学科分类号：

摘要：

This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems. © 2014, ISAROB.

引用

页码：201 / 208

页数：7

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