Performance comparison of self-adaptive and adaptive differential evolution algorithms

被引：180

作者：

Brest, Janez ^{[1
]}

Boskovic, Borko ^{[1
]}

Greiner, Saso ^{[1
]}

Zumer, Viljem ^{[1
]}

Maucec, Mirjam Sepesy ^{[1
]}

机构：

[1] Univ Maribor, Fac Elect Engn & Comp Sci, SLO-2000 Maribor, Slovenia

来源：

SOFT COMPUTING | 2007年 / 11卷 / 07期

关键词：

differential evolution; control parameter; fitness function; optimization; self-adaptation;

D O I：

10.1007/s00500-006-0124-0

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) has been shown to be a simple, yet powerful, evolutionary algorithm for global optimization for many real problems. Adaptation, especially self-adaptation, has been found to be highly beneficial for adjusting control parameters, especially when done without any user interaction. This paper presents differential evolution algorithms, which use different adaptive or self-adaptive mechanisms applied to the control parameters. Detailed performance comparisons of these algorithms on the benchmark functions are outlined.

引用

页码：617 / 629

页数：13

共 25 条

[1] Population set-based global optimization algorithms:: some modifications and numerical studies [J].