Title :
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
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;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6244137
