Research on decomposition-based multi-objective evolutionary algorithm with dynamic weight vector

被引:2
作者
Zhao, Jiale [1 ,3 ]
Huang, Xiangdang [2 ,3 ]
Li, Tian [2 ,3 ]
Yu, Huanhuan [2 ,3 ]
Fei, Hansheng [2 ,3 ]
Yang, Qiuling [2 ,3 ]
机构
[1] Hainan Univ, Sch Informat & Commun Engn, Haikou 570228, Peoples R China
[2] Hainan Univ, Sch Comp Sci & Technol, Haikou 570228, Peoples R China
[3] Hainan Univ, Innovat Platform Academicians Hainan Prov, Haikou 570228, Peoples R China
关键词
Multi -objective evolutionary algorithm; Decomposition; Dynamic weight vector; Preference distribution; Combination evolution operator; OPTIMIZATION ALGORITHM; DOMINANCE; MOEA/D;
D O I
10.1016/j.jocs.2024.102361
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In recent years, multi -objective evolutionary algorithm based on decomposition has gradually attracted people 's interest. However, this algorithm has some problems. For example, the diversity of the algorithm is poor, and the convergence and diversity of the algorithm are unbalanced. In addition, users don 't always care about the entire Pareto front. Sometimes they may only be interested in specific areas of entire Pareto front. Based on the above problems, this paper proposes a decomposition -based multi -objective evolutionary algorithm with dynamic weight vector (MOEA/D-DWV). Firstly, a weight vector generation model with uniform distribution or preference distribution is proposed. Users can decide which type of weight vector to generate according to their own wishes. Then, two combination evolution operators are proposed to better balance the convergence and diversity of the algorithm. Finally, a dynamic adjustment strategy of weight vector is proposed. This strategy can adjust the distribution of weight vector adaptively according to the distribution of solutions in the objective space, so that the population can be uniformly distributed in the objective space as much as possible. MOEA/D-DWV algorithm is compared with 9 advanced multi -objective evolutionary algorithms. The comparison results show that MOEA/D-DWV algorithm is more competitive. Data availability: Data will be made available on request.
引用
收藏
页数:20
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