文献类型：期刊文献

英文题名：An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization

作者：Gao, Mengqi[1,2];Feng, Xiang[1,2];Yu, Huiqun[1,2];Li, Xiuquan[3]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Engn Res Ctr Smart Energy, Shanghai, Peoples R China;[3]Chinese Acad Sci & Technol Dev, Beijing 100038, Peoples R China

年份：2023

卷号：53

期号：18

起止页码：21116

外文期刊名：APPLIED INTELLIGENCE

收录：;EI(收录号：20232114122641);WOS:【SCI-EXPANDED(收录号:WOS:000991515700003)】；

基金：This work is supported by the National Natural Science Foundation of China (No.62276097), Key Program of National Natural Science Foundation of China (No.62136003), National Key Research and Development Program of China (No. 2020YFB1711700), Special Fund for Information Development of Shanghai Economic and Information Commission (No.XX-XXFZ-02-20-2463) and Scientific Research Program of Shanghai Science and Technology Commission (No.21002411000).

语种：英文

外文关键词：Large-scale; Sparse multiobjective optimization; Evolutionary computation; Deep reinforcement learning

摘要：Large-scale sparse multiobjective optimization problems (SMOPs) widely exist in academic research and engineering applications. The curse of dimensionality and the fact that most decision variables take zero values make optimization very difficult. Sparse features are common to many practical complex problems currently, and using sparse features as a breakthrough point can enable many large-scale complex problems to be solved. We propose an efficient evolutionary algorithm based on deep reinforcement learning to solve large-scale SMOPs. Deep reinforcement learning networks are used for mining sparse variables to reduce the problem dimensionality, which is a challenge for large-scale multiobjective optimization. Then the three-way decision concept is used to optimize decision variables. The emphasis is on optimizing deterministic nonzero variables and continuously mining uncertain decision variables. Experimental results on sparse benchmark problems and real-world application problems show that the proposed algorithm performs well on SMOPs while being highly efficient.