A MULTIOBJECTIVE EVOLUTIONARY ALGORITHM USING DYNAMIC WEIGHT DESIGN METHOD

被引：0
作者：
Gu, Fangqing [1 ]
Liu, Hai-lin [1 ]
Tan, Kay Chen [2 ]
机构：
[1] Guangdong Univ Technol, Fac Appl Math, Guangzhou, Guangdong, Peoples R China
[2] Natl Univ Singapore, Fac Engn, Singapore 117548, Singapore
来源：
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2012年 / 8卷 / 5B期
关键词：
Equidistant interpolation; Multiobjective optimization; Evolutionary algorithm; Uniformly distribution; DIFFERENTIAL EVOLUTION;
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：
In most rnultiobjective evolutionary algorithms (MOEA) based on aggregating objectives, the weight vectors are user-supplied or generated randomly, and they are static in the algorithms. If the Pareto front (PF) shape is not complex, the algorithms can find a set of uniformly distributed Pareto optimal solutions along the PF; otherwise, they might fail. A dynamic weight design method based on the projection of the current nondominant solutions and equidistant interpolation is proposed in this paper. Even if the PF is complex, we can find evenly distributed Pareto optimal solutions by this method. Some test instances are constructed to compare the performance of the MOEA/D using dynamic weight design method with that of MOEA/D. The results indicate that the dynamic weight design method can dramatically improve the performance of the algorithms.
引用
收藏
页码：3677 / 3688
页数：12
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