A Multi-UAV Path Planning Method for Simultaneous Arrival Based on an Improved Mayfly Algorithm

To address the problem of simultaneous arrival path planning for multi-UAV in complex scenarios, this paper proposes an improved mayfly algorithm. Aiming at the shortcomings of the traditional mayfly algorithm, such as high randomness in population initialization, slow convergence speed, and poor satisfaction of simultaneous arrival constraints, the Halton sequence and an adaptive Gaussian-Cauchy mutation strategy are introduced to balance the algorithm's global exploration and local exploitation capabilities. This improvement further enhances the algorithm's optimization ability, convergence performance, and solution efficiency, thereby enabling multiple UAVs to arrive at the same target point simultaneously in complex scenarios. By comparing the simulation results of the traditional mayfly algorithm in a 3D urban scenario, the effectiveness of the proposed algorithm is demonstrated.