A reinforcement learning-assisted differential evolution with population feature replay

As an effective global optimization method, differential evolution (DE) often faces limitations in search capability due to its differential mutation strategy and control parameters. To effectively address diverse problems, adaptively adjusting the control parameter and mutation strategy has become an important research direction. Motivated by this challenge, a reinforcement learning-assisted differential evolution with population feature replay (RLDE-PFR) is proposed. In RLDE-PFR, DE can autonomously adjust mutation strategies and control parameters based on the state of the population. In addition, the proposed population feature replay mechanism (PFR) fully exploits historical successful information to guide parameter generation. To comprehensively validate the performance of RLDE-PFR, we compared it with six state-of-the-art algorithms. The experiments were conducted on the CEC2015 and CEC2017 test suites and a practical Transformer hyperparameter optimization problem. The effectiveness of both reinforcement learning-based autonomous adjustment and the PFR is also validated. The experimental results indicate that RLDE-PFR exhibits competitive and superior performance in terms of resulting accuracy and search efficiency. The RLDE-PFR code is available at https://github.com/Strive-code/rl-pfr.git .

Duke Scholars

Author Feng Tian DKU Faculty