UAV 3D Path Planning Based on Improved Chimp Optimization Algorithm

Aiming to address the limitations of the standard Chimp Optimization Algorithm (ChOA), such as inadequate search ability and susceptibility to local optima in Unmanned Aerial Vehicle (UAV) path planning, this paper proposes a three-dimensional path planning method for UAVs based on the Improved Chimp Optimization Algorithm (IChOA). First, this paper models the terrain and obstacle environments spatially and formulates the total UAV flight cost function according to the constraints, transforming the path planning problem into an optimization problem with multiple constraints. Second, this paper enhances the diversity of the chimpanzee population by applying the Sine chaos mapping strategy and introduces a nonlinear convergence factor to improve the algorithm’s search accuracy and convergence speed. Finally, this paper proposes a dynamic adjustment strategy for the number of chimpanzee advance echelons, which effectively balances global exploration and local exploitation, significantly optimizing the algorithm’s search performance. To validate the effectiveness of the IChOA algorithm, this paper conducts experimental comparisons with eight different intelligent algorithms. The experimental results demonstrate that the IChOA outperforms the selected comparison algorithms in terms of practicality and robustness in UAV 3D path planning. It effectively solves the issues of efficiency in finding the shortest path and ensures high stability during execution.