A novel mutation differential evolution for global optimization

被引：21

作者：

Yu, Xiaobing ^{[1
,2
,3
]}

Cai, Mei ^{[2
,3
]}

Cao, Jie ^{[1
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing 210044, Jiangsu, Peoples R China

[2] Nanjing Univ Informat Sci & Technol, China Inst Mfg Dev, Nanjing 210044, Jiangsu, Peoples R China

[3] Nanjing Univ Informat Sci & Technol, Sch Econ & Management, Nanjing 210044, Jiangsu, Peoples R China

来源：

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2015年 / 28卷 / 03期

关键词：

Evolutionary algorithm; global optimization; differential evolution; DE/best/2; particle swarm optimization; PARTICLE SWARM; PARAMETERS; ALGORITHM;

D O I：

10.3233/IFS-141388

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 simple and powerful population-based evolutionary algorithm. The success of DE in solving a specific problem crucially depends on appropriately choosing generation strategies and control parameter values. A novel mutation DE (MDE) is proposed to improve generation strategy DE/best/2. Adaptive mutation is conducted to current population when the population clusters around local optima. Control parameters are adapted based on constants. The performance of MDE is extensively evaluated on eighteen benchmark functions and compares favorably with the several DE variants.

引用

页码：1047 / 1060

页数：14

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