An improved multi-objective differential evolution algorithm

Author

Niu, Dapeng ; Wang, Fuli ; Chang, Yuqing ; He, Dakuo ; Gu, Dehao

Author_Institution

Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China

fYear

2012

fDate

23-25 May 2012

Firstpage

879

Lastpage

882

Abstract

Based on multi-objective differential evolution algorithm, adaptive chaotic multi-objective differential evolution algorithm (AC-DEMO) is proposed, combining with adaptive and chaotic principles. In AC-DEMO, chaotic initialization and adaptive mutation operator are introduced to improve the efficiency of the algorithm. Numerical experiment results of commonly used test functions show that the algorithm has a good approximation and uniformity index and is suitable to solve complex multi-objective optimization problems.

Keywords

approximation theory; evolutionary computation; AC-DEMO; adaptive chaotic multiobjective differential evolution algorithm; adaptive mutation operator; chaotic initialization; good approximation; improved multiobjective differential evolution algorithm; uniformity index; Algorithm design and analysis; Approximation algorithms; Educational institutions; Evolutionary computation; Measurement; Optimization; Vectors; Multi-objective; adaptive mutation; approximation and uniformity; chaotic initialization; differential evolution algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2012 24th Chinese

Conference_Location

Taiyuan

Print_ISBN

978-1-4577-2073-4

Type

conf

DOI

10.1109/CCDC.2012.6244137

Filename

6244137