Differential Evolution with Joint Adaptation of Mutation Strategies and Control Parameters via Distributed Proximal Policy OptimizationOA
Differential Evolution with Joint Adaptation of Mutation Strategies and Control Parameters via Distributed Proximal Policy Optimization
Wenjie Ding;Mengtao Qian;Chao Lu;Jin Yi;Huayan Pu;Jun Luo
State Key Labortary of Mechanical Transmission for Advanced Equipment and School of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,ChinaState Key Labortary of Mechanical Transmission for Advanced Equipment and School of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,ChinaSchool of Computer Science,China University of Geosciences,Wuhan 430078,ChinaState Key Laboratory of Mechanical Transmission for Advanced Equipment and School of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China||State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou 310027,ChinaState Key Labortary of Mechanical Transmission for Advanced Equipment and School of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,ChinaState Key Labortary of Mechanical Transmission for Advanced Equipment and School of Mechanical and Vehicle Engineering,Chongqing University,Chongqing 400044,China
Differential Evolution(DE)Evolutionary Algorithm(EA)Deep Reinforcement Learning(DRL)parameter control
Differential Evolution(DE)Evolutionary Algorithm(EA)Deep Reinforcement Learning(DRL)parameter control
《清华大学学报自然科学版（英文版）》 2026 (1)
101-124,24
This work was supported by the National Natural Science Foundation of China(No.52105244),the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(No.GZKF-202425),the Entrepreneurship and Innovation Support Plan of Chongqing for Returned Overseas Scholars(No.cx2023085),and the Independent Research Project-Key Program from the State Key Laboratory of Mechanical Transmission for Advanced Equipment(No.SKLMT-ZZKT-2024Z09).
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