High-Dimensional Function Optimization with a Self Adaptive Differential Evolution

被引：3
|
作者：
Worasucheep, Chukiat [1 ]
机构：
[1] King Mongkuts Univ Technol Thonburi, Dept Math, Fac Sci, Thonburi, Thailand
来源：
2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年
关键词：
Differential Evolution; Evolutionary Algorithm; High dimensional; Scalability; Self-Adaptation; ALGORITHM;
D O I：
10.1109/ICICISYS.2009.5357711
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A good optimization algorithm must be capable of handling high-dimensional problems, meaning that there are many decision variables to be optimized at the same time The problems of this category arc challenging This paper tests the scalability of wDE, which is a differential evolution algorithm with self-adaptive parameters The statistical results and Convergence graphs from the experimentation using benchmark problems of 100-, 500-, and 2000-dimensions are analyzed and compared to three standard variants of differential evolution algorithm
引用
收藏
页码：668 / 673
页数：6
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