Multi-objective optimization based on improved differential evolution algorithm

被引：0

作者：

Wang, Shuqiang ^{[1
]}

Ma, Jianli ^{[2
]}

机构：

[1] School of Information and electrical engineering, Hebei University of Engineering, Handan, Hebei

[2] Suburban water and power supply management office of Handan City, Handan, Hebei

来源：

Telkomnika (Telecommunication Computing Electronics and Control) | 2014年 / 12卷 / 04期

关键词：

Differential evolution; Effect of parameters; Multi-objective optimization; Numerical experiment;

D O I：

10.12928/TELKOMNIKA.v12i4.531

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions.

引用

页码：977 / 984

页数：7

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