Differential Evolution with Improved Mutation Strategy

被引：0

作者：

Wan, Shuzhen ^{[1
]}

Xiong, Shengwu ^{[1
]}

Kou, Jialiang ^{[1
]}

Liu, Yi ^{[1
]}

机构：

[1] Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430070, Peoples R China

来源：

ADVANCES IN SWARM INTELLIGENCE, PT I | 2011年 / 6728卷

关键词：

trigonometric differential evolution; differential evolution; benchmark function; crossover operation; OPTIMIZATION;

D O I：

暂无

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 is a powerful evolution algorithm for optimization of real valued and multimodal functions. To accelerate its convergence rate and enhance its performance, this paper introduces a top-p-best trigonometric mutation strategy and a self-adaptation method for controlling the crossover rate (CR). The performance of the proposed algorithm is investigated on a comprehensive set of 13 benchmark functions. Numerical results and statistical analysis show that the proposed algorithm boosts the convergence rate yet maintaining the robustness of the DE algorithm.

引用

页码：431 / 438

页数：8

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