A new mutation operator for differential evolution algorithm

被引：13
|
作者：
Zuo, Mingcheng [1 ]
Dai, Guangming [2 ,3 ]
Peng, Lei [2 ,3 ,4 ]
机构：
[1] China Univ Min & Technol, Artificial Intelligence Res Inst, Xuzhou 221116, Jiangsu, Peoples R China
[2] China Univ Geosci Wuhan, Sch Comp Sci, 388 LuMo Rd, Wuhan, Peoples R China
[3] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan, Peoples R China
[4] Minist Educ, Key Lab Geol Survey & Evaluat, Wuhan, Peoples R China
来源：
SOFT COMPUTING | 2021年 / 25卷 / 21期
关键词：
Differential evolution; Mutation operator; Scaling factor; PARAMETERS;
D O I：
10.1007/s00500-021-06077-6
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The widely employed mutation operator DE/current - to- pbest/1 in the differential evolution algorithm (DE) is further developed to a newversion DE/current- to- pbest/1- X in this paper. To test its performance, it has been embedded in the novel successful history-based adaptive DE (L-SHADE) and compared with other recently proposed mutation operators. In DE/current - to- pbest/1- X, the updated parameter memories in each generation are not adopted when the initial value can still maintain an acceptable successful rate of finding better offspring. Also, the generatedworse offsprings with acceptable fitness values are partially archived to generate differential vectors. The experimental results show that DE/current - to - pbest/1 - X has a comparable performance than DE/current - to - pbest/1, DE/current - to - ord_ pbest/1 and DE/current - to - ord_best/1.
引用
收藏
页码：13595 / 13615
页数：21
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