A hybrid of differential evolution and genetic algorithm for constrained multiobjective optimization problems

被引：0
|
作者：
Zhang, Min [1 ]
Geng, Huantong [1 ]
Luo, Wenjian [1 ]
Huang, Linfeng [1 ]
Wang, Xufa [1 ]
机构：
[1] Univ Sci & Technol China, Dept Comp Sci & Technol, Nat Inspired Computat & Applicat Lab, Hefei 230027, Anhui, Peoples R China
来源：
SIMULATED EVOLUTION AND LEARNING, PROCEEDINGS | 2006年 / 4247卷
关键词：
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：
Two novel schemes of selecting the current best solutions for multiobjective differential evolution are proposed in this paper. Based on the search biases strategy suggested by Runarsson and Yao, a hybrid of multiobjective differential evolution and genetic algorithm with (N+N) framework for constrained MOPs is given. And then the hybrid algorithm adopting the two schemes respectively is compared with the constrained NSGA-II on 4 benchmark functions constructed by Deb. The experimental results show that the hybrid algorithm has better performance, especially in the distribution of non-dominated set.
引用
收藏
页码：318 / 327
页数：10
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