Research of UAV 3D path planning based on improved Dwarf mongoose algorithm with multiple strategies

Unmanned Aerial Vehicles (UAVs) consistently encounter complex operational environments during task execution. To enhance UAV adaptability in such environments, improve rapid and efficient path planning capabilities, and reduce operational costs, this paper proposes a 3D UAV path planning algorithm based on an improved Dwarf Mongoose Optimization (DMO) algorithm enhanced with multiple strategies. Initially, a chaos mapping-based opposition-based learning strategy is introduced to ensure a uniform distribution of the initial population in the solution space, thereby enhancing diversity and improving global search performance. Then, a golden sine function based on nonlinear weights is employed to help dwarf mongooses avoid getting trapped in local optima and to balance global exploration with local exploitation. In addition, a differential mutation strategy is incorporated, which uses difference information between individuals to guide the evolutionary process, further improving diversity and enhancing the ability to escape local optima. The efficacy of the improved algorithm, in terms of convergence precision, the ability to escape local optima, and a balanced exploration and exploitation capability, is demonstrated through ablation experiments and the Wilcoxon rank-sum test. Comparative evaluations on benchmark test functions demonstrate that the improved algorithm (CDMOS) outperforms the original DMO in optimization performance, convergence precision, and overall stability, achieving an average improvement of 53.5% in convergence accuracy and 35.1% in solution stability across 29 benchmark functions. Finally, the improved algorithm is applied to 3D map path planning simulations involving multiple nodes and obstacles, confirming its capability to enhance UAV robustness, adaptability, and real-time performance. In these simulations, CDMOS reduced path length by 46.0%, smoothness cost by 93.4%, and maintained a low obstacle cost, thus generating shorter, smoother, and safer flight paths. The generated flight paths are optimized in terms of both stability and efficiency, making the algorithm suitable for complex mission scenarios.