Abstract: Aiming at the shortcomings that crow search algorithm is easy to fall into local optimization and location update strategy is blind, an adaptive crow search algorithm based on Levy flight is proposed, which dynamically adjusts the perception probability and flight distance of the standard crow search algorithm, and optimizes the location update strategy of crow individuals in the second case. The proposed algorithm introduces Levy flight, experience factor and adaptive adjustment parameter mechanism, dynamically increase the global search ability in the early stage and the local optimization ability in the later stage of the algorithm. When the algorithm individual finds that he is tracked by other individuals, it adopts the update strategy of combining empirical factors and Levy flight to guide other individuals, enhance the efficiency of individual location update, avoid individual oscillation around the optimal solution, and make the algorithm reach the extreme point quickly and accurately, it effectively makes up for the blindness of location update and slow convergence speed of the original crow search algorithm. Through the experimental comparison with other new intelligent optimization algorithms in 8 benchmark functions and 1 engineering application problem, the effectiveness of the algorithm is tested. The simulation results show that the optimization average result, standard deviation, convergence and robustness of the proposed algorithm are better than other algorithms, which effectively avoids the blindness of location update and enhances the performance efficiency of the algorithm.