An Improved Dung Beetle Optimizer Based on Fuch Chaotic Map and Its Application in 3D Path Planning for Unmanned Aerial Vehicles

Aiming at the problems of low convergence efficiency and easy to fall into the local optimal path of Dung beetle path planning algorithm in complex three-dimensional environment, this paper proposes an improved Dung beetle optimization algorithm based on Fuch chaotic map (DBO-FUCH). The algorithm optimizes population initialization by the high ergodicity of Fuch chaotic map and the strong spatial diffusion ability of the solution, and generates initial solutions with more uniform distribution to enhance diversity. Combining the linear decay of inertia weight and the inverse cosine decay strategy of mutation scale, the search granularity was dynamically adjusted to balance global exploration and local exploitation. A stealing competition mechanism was designed to drive low fitness individuals to migration to the elite solution region, and chaotic disturbance was used to avoid convergence and stagnation. In the 3D simulation environment with 100 peaks, the average path length of DBO-Fuch algorithm is 810 m, which is 10% and 11% shorter than that of standard Qiang Insect Optimization algorithm (DBO) and gray Wolf optimization algorithm (GWO), the convergence speed is increased by 25%, and the standard deviation of trajectory curvature fluctuation is reduced by 28%. The efficiency and smoothness of path planning are significantly improved. Friedman rank test shows that DBO-FUCH has the best comprehensive performance, which verifies the influence of chaotic mapping and adaptive strategy on the performance of the algorithm. This study provides a new scheme for efficient path planning of Uavs in complex terrain.