In view of the shortcomings of UAV path planning in 3D complexity environment such as search stagnation and convergence to local optimization,an improved chimp optimization algorithm based on multiple strategies is proposed.To address the shortcomings of the optimization accuracy of the chimp algorithm,a convergence factor nonlinear update strategy is introduced to balance the global search and local development capabilities.A weight factor is designed to avoid the blindness of individual following and the assimilation of individuals in the later stage of iteration for improving the search accuracy.A golden sine Levy flight guidance mechanism is designed to prevent falling into local optimization due to the gradual dilution of diversity.The improved chimp optimization algorithm is used to solve the UAV path planning problem for constructing a path planning model by use of 3D terrain map of the UAV flight environment,designing a multi-constraint flight cost function and using it as a fitness function,and iteratively solving the UAV 3D path planning scheme.The results show that the improved algorithm can search for a safe obstacle avoidance path with lower trajectory cost,and the search accuracy is higher than that of analog algorithms.