In order to solve the UAV path planning problem in complex three-dimensional environment，the traditional Dung Bettle Optimizer (DBO) algorithm and Grey Wolf Optimizer (GWO) algorithm are combined to propose a DBO-GWO algorithm.Firstly，the algorithm combines chaos strategy with quasi-reflection strategy to generate the initial population to fully explore the solution space.Secondly，the foraging behavior of the DBO algorithm and the position update strategy of the GWO algorithm are combined to help the population get rid of the local optimum and further improve the performance of the algorithm.Finally，Levy flight strategy and dynamic weight coefficient improvement algorithm are adopted to improve the diversity of the population in the later stage of the algorithm，and balance the exploration ability and the convergence speed of the algorithm. By testing with benchmark functions and conducting simulation experiments，the results demonstrate that the proposed algorithm shows faster convergence speed and the ability to generate better paths in the same environment，which shows that the proposed algorithm can effectively solve the path planning problem of UAVs.