Improved differential evolution with dynamic mutation parameters

被引：4
|
作者：
Lin, Yifeng [1 ]
Yang, Yuer [1 ]
Zhang, Yinyan [1 ,2 ]
机构：
[1] Jinan Univ, Coll Cyber Secur, Guangzhou 511436, Peoples R China
[2] Pazhou Lab, Guangzhou, Peoples R China
来源：
SOFT COMPUTING | 2023年 / 27卷 / 23期
基金：
中国国家自然科学基金;
关键词：
Differential evolution (DE) Algorithm; Global optimization; Scheme optimization; Test function; ALGORITHM; OPTIMIZATION;
D O I：
10.1007/s00500-023-09080-1
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) algorithms tend to be limited to local optimization when solving complex optimization problems. Different iteration schemes lead to different convergence speeds. In this paper, we mainly use the dynamic mutation parameter FS to improve the DE algorithm. Based on two ideas, a total of seven DE schemes are proposed to optimize the DE algorithm. We test the performance of the improved DE scheme on 56 test functions. Experiments show that the improved DE algorithm is better than the baseline DE algorithm in terms of accuracy, convergence and8 convergence speed.
引用
收藏
页码：17923 / 17941
页数：19
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