An adaptive search strategy combination algorithm based on reinforcement learning and neighborhood search

Abstract Integrating multiple search operators to utilize their different characteristics in order to improve the performance of evolutionary algorithms is a challenging task. This paper proposes an adaptive combination algorithm that integrates four search operators, called RLACA. RLACA introduces a reinforcement learning-based adaptive search operator selection mechanism (RLAS) to dynamically choose the most suitable search operator based on the individual states. Additionally, a neighborhood search strategy based on differential evolution (NSDE) is incorporated to mitigate premature convergence by increasing population diversity. To verify the effectiveness of the proposed algorithm, a comprehensive testing was conducted using the CEC2017 test suite. The experimental results demonstrate that RLAS can adaptively select a suitable search operator and NSDE can enhance the algorithm’s local search capability, thereby improving the performance of RLACA. Compared with the four basic algorithms and four combination algorithms, RLACA performs better in both convergence speed and resolution accuracy.