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

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

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  • 国家科技期刊平台
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Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted consider-able attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and nec-essary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 bench-mark problems.The experimental results reveal that the pro-posed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

Constrained multi-objective optimizationdeep Q-learningdeep reinforcement learning(DRL)evolutionary algo-rithmsevolutionary operator selection

Fei Ming、Wenyin Gong、Ling Wang、Yaochu Jin

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School of Computer Science, China University of Geosciences, Wuhan 430074, China

Department of Automation, Tsinghua University, Beijing 100084, China

Faculty of Technology, Bielefeld University, North Rhine-Westphalia, 33619 Bielefeld, Germany

国家自然科学基金国家自然科学基金Natural Science Foundation for Distinguished Young Scholars of Hubei

62076225620733002019CFA081

2024

10.1109/JAS.2023.123687

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(4)
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