Autoencoder evolutionary algorithm for large-scale multi-objective optimization problem

被引:0
作者
Hu, Ziyu [1 ,2 ]
Xiao, Zhixing [1 ,2 ]
Sun, Hao [1 ,2 ]
Yang, He [3 ]
机构
[1] Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Hebei, Peoples R China
[2] Yanshan Univ, Key Lab Ind Comp Control Engn Hebei Prov, Qinhuangdao 066004, Hebei, Peoples R China
[3] Tianjin Res Inst Elect Sci, Tianjin 300180, Peoples R China
基金
中国国家自然科学基金;
关键词
Large-scale; Multi-objective optimization; Evolutionary algorithms; Dimensionality reduction;
D O I
10.1007/s13042-024-02221-4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Multi-objective optimization problems characterized by a substantial number of decision variables, which are also called large-scale multi-objective optimization problems (LSMOPs), are becoming increasingly prevalent. Traditional evolutionary algorithms may deteriorate drastically when tackling a large number of decision variables. For LSMOPs, the dimensionality of the decision variables needs to be reduced and the algorithm needs to be designed according to the characteristics of divide-and-conquer. The autoencoder evolutionary algorithm (AEEA) is proposed based on autoencoder dimensionality reduction, the grouping of decision variables, and the application of divide-and-conquer strategies. The proposed algorithm is compared with other classical algorithms. The experiment result shows that AEEA achieves excellent convergence and diversity, and still performs well in decision variables of higher dimensions. Finally, it is verified that the autoencoder improves the running time of the proposed algorithm.
引用
收藏
页码:5159 / 5172
页数:14
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