Improved Differential Evolutions Using a Dynamic Differential Factor and Population Diversity

被引：1

作者：

Cheng, Jixiang ^{[1
]}

Zhang, Gexiang ^{[2
]}

机构：

[1] Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu, Peoples R China

[2] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu, Peoples R China

来源：

2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL I, PROCEEDINGS | 2009年

基金：

中国国家自然科学基金;

关键词：

differential evolution; differential factor; differential strategy; population diversity;

D O I：

10.1109/AICI.2009.151

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As a new kind of evolutionary algorithms, differential evolution (DE) has attracted much attention in solving optimization problems in the last few years. To accelerate its convergence rate and enhance its performances, this paper introduces a dynamic adjustment method for the differential factor and a modified version of mutation strategy into DE. Furthermore, a disturbance approach based on population diversity is used to further improve the search capability. Thus, two improved DE, IDE1 and IDE2, are presented. The performances of the IDE1 and IDE2 are evaluated on seven complex benchmark functions with three different dimensionalities. Experimental results show that the performances of IDE1 and IDE2 are superior to other two DEs in terms of convergence rates and qualities of solutions.

引用

页码：402 / +

页数：2

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