Optimized 3D path planning for UAVs using a multi-strategy snow avalanches algorithm

Unmanned aerial vehicle (UAV) path planning is critical for autonomous flight operations, yet existing algorithms face challenges in balancing optimization accuracy and computational efficiency. In this paper, we proposed a Multi-Strategy Snow Avalanches Algorithm (MSAA) to address the limitations of the original Snow Avalanches Algorithm (SAA), which suffered from low convergence precision and local optima entrapment. The MSAA integrated three key enhancements: a chaos theory-based initialization method to enhance population diversity, a stochastic central learning mechanism for dynamic exploration–exploitation balance, and an improved sine–cosine operator to prevent premature convergence. Ultimately, the MSAA was successfully applied to the challenge of 3D UAV path planning. By integrating cubic B-spline interpolation for smooth trajectory generation, the proposed method achieved impressive results, with mean path error rates of 0.5% in low-dimensional terrains and 0.4% in high-dimensional environments. Experimental validations demonstrated superior convergence speed and computational efficiency compared to benchmark algorithms, confirming its practical value for 3D UAV navigation in complex scenarios.