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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\u2019s local search capability, thereby improving the performance of RLACA. 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