Improved Differential Evolution via Cuckoo Search Operator

被引：0
|
作者：
Musigawan, Pakarat [1 ]
Chiewchanwattana, Sirapat [1 ]
Sunat, Khamron [1 ]
机构：
[1] Khon Kaen Univ, Dept Comp Sci, Khon Kaen 40002, Thailand
来源：
NEURAL INFORMATION PROCESSING, ICONIP 2012, PT I | 2012年 / 7663卷
关键词：
Differential evolution; Cuckoo search; Optimization; Global search; 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 (DE) is a very popular optimization algorithm for solving numerical optimization problems. It is simple yet powerful algorithm, which has shown effective performance in many optimization problems. In this paper, DECSO that uses the Abandon operator of Cuckoo search to improve the exploration ability of the original DE was proposed. The experimental studies on ten well-known benchmark functions have shown that the proposed approach has efficient search power and fast convergence.
引用
收藏
页码：465 / 472
页数：8
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