A multi-mechanism chaotic evolution approach for 3D path planning of UAVs

3D UAV path planning intends to produce safe, feasible, and optimized flight trajectories in complex 3D environments containing multiple obstacles and coupled constraints. Nevertheless, many existing optimization approaches encounter difficulties when dealing with high-dimensional constrained search spaces, since limited population diversity and premature convergence can significantly reduce optimization effectiveness. To overcome these issues, this paper presents an improved chaotic evolution optimization (ICEO) algorithm that combines several collaborative optimization strategies. Initially, a good point set initialization method is introduced to enhance the uniformity and diversity of the initial population distribution. Subsequently, a multi-scale gradient memory mechanism is developed to provide adaptive search guidance by utilizing historical evolutionary information. In addition, an adaptive Lévy flight strategy is incorporated to enhance global exploration capability while balancing exploration and exploitation performance in a more effective manner. Experimental assessments on the CEC benchmark functions show that ICEO achieves competitive convergence precision and stable optimization behavior in comparison with several representative metaheuristic algorithms. Moreover, in simulated 3D UAV path planning scenarios, the proposed approach can produce feasible, smooth, and comparatively efficient flight trajectories under multiple coupled constraints. These findings suggest that the proposed method has promising potential for constrained trajectory optimization tasks in complex 3D environments.