Research on UAV Path Planning Based on an Improved Dwarf Mongoose Algorithm with Multi-strategy Fusion

To address the issues of the Dwarf Mongoose Optimization Algorithm (DMOA) being prone to local optima and exhibiting low convergence accuracy during its operation, this paper introduces the Chaotic Dwarf Mongoose Optimization Algorithm (CDMOA). The CDMO algorithm employs a chaos mapping strategy to ensure a uniform distribution of the initial population across the solution space, thereby enhancing population diversity. Additionally, it utilizes an inverse learning strategy to bolster the global search capabilities of the algorithm. Comparative experiments conducted using benchmark test functions demonstrate that CDMOA outperforms the original DMO algorithm in terms of optimization performance, convergence accuracy, and algorithm stability. Finally, the application of CDMOA to drone flight path planning is presented. The simulation results indicate that the optimized flight paths generated by the improved algorithm are superior and more stable.