Modified Mutation Operators for Differential Evolution

被引：0

作者：

Kumar, Pravesh ^{[1
]}

Pant, Millie ^{[1
]}

Singh, V. P. ^{[2
]}

机构：

[1] Indian Inst Technol, Roorkee, Uttar Pradesh, India

[2] Millennium Inst Technol, Saharanpur, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SOFT COMPUTING FOR PROBLEM SOLVING (SOCPROS 2011), VOL 1 | 2012年 / 130卷

关键词：

Optimization; Differential evolution; Mutation;

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) has emerged as a popular tool for solving global optimization problems occurring in various fields. However, like most of the population based random search techniques in their basic form, DE has some inherent drawbacks like slow and/or premature convergence, stagnation etc. which sometimes hinders its performance. In the present study we propose two new mutation schemes for DE to enhance its performance in terms of solution quality as well as convergence rate.

引用

页码：579 / +

页数：3

共 17 条

[11] Accelerating differential evolution using an adaptive local search [J].