A Gene-Level Hybrid Crossover Operator for Multiobjective Evolutionary Algorithm

被引:4
|
作者
Zhu, Qingling [1 ]
Lin, Qiuzhen [1 ]
Chen, Jianyong [1 ]
Huang, Peizhi [1 ]
机构
[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
关键词
evolutionary algorithm; simulated binary crossover; differential evolution; hybrid crossover; OPTIMIZATION;
D O I
10.1109/ISCMI.2015.25
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This study proposes a novel recombination operator, called hybrid crossover operator (HX), which is performed in gene level of chromosome to enhance the optimization performance of multi-objective evolutionary algorithms (MOEAs). The proposed HX operator combines the advantages of simulated binary crossover with local search ability and differential evolution with strong global search capability. When HX is embedded into two state-of-the-art MOEAs, i.e., NSGA-II and MOEA/D-DE, the experimental results validate the improvement of HX when compared to the original counterpart.
引用
收藏
页码:20 / 24
页数:5
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