Reinforcement Learning-Based Differential Evolution Algorithm with Levy Flight

In this paper, a reinforcement learning-based differential evolution algorithm with levy flight strategy (RLLDE) for solving optimization problems is proposed. It introduces a novel mutation mode considering search directions is proposed firstly. Secondly, a levy flight strategy is employed to enhance the exploration capability of Differential Evolution (DE). Lastly, the Q-learning method from reinforcement learning is introduced to establish a switching mechanism between two different updating modes during the mutation stage. These strategies effectively improve the algorithm’s convergence speed and accuracy. RLLDE is analyzed on CEC 2017 benchmark functions to validate its optimization performance. Compared to five basic DE and eight efficient optimizers, the experimental results demonstrate that the algorithm exhibits efficient and effective performance in solving optimization problems.