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Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Fei Ming Wenyin Gong Ling Wang Yaochu Jin

自动化学报（英文版）2024，Vol.11Issue(4)：919-931,13.

自动化学报（英文版）2024，Vol.11Issue(4)：919-931,13.DOI:10.1109/JAS.2023.123687

Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

Fei Ming ¹Wenyin Gong ¹Ling Wang ²Yaochu Jin³

作者信息

1. School of Computer Science, China University of Geosciences, Wuhan 430074, China
2. Department of Automation, Tsinghua University, Beijing 100084, China
3. Faculty of Technology, Bielefeld University, North Rhine-Westphalia, 33619 Bielefeld, Germany
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摘要

关键词

Constrained multi-objective optimization/deep Q-learning/deep reinforcement learning(DRL)/evolutionary algo-rithms/evolutionary operator selection

Key words

Constrained multi-objective optimization/deep Q-learning/deep reinforcement learning(DRL)/evolutionary algo-rithms/evolutionary operator selection

引用本文复制引用

Fei Ming,Wenyin Gong,Ling Wang,Yaochu Jin..Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection[J].自动化学报（英文版）,2024,11(4):919-931,13.

基金项目

This work was partly supported by the National Natural Science Foundation of China(62076225,62073300)and the Natural Science Foundation for Distinguished Young Scholars of Hubei(2019CFA081). （62076225,62073300）

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