Single unmanned aerial vehicle (UAV) multitasking plays an important role in multiple UAVs cooperative control, which is as well as the most complicated and hardest part. This paper establishes a three-dimensional topographical map, and an improved adaptive differential evolution (IADE) algorithm is proposed for single UAV multitasking. As an optimized problem, the efficiency of using standard differential evolution to obtain the global optimal solution is very low to avoid this problem. Therefore, the algorithm adopts the mutation factor and crossover factor into dynamic adaptive functions, which makes the crossover factor and variation factor can be adjusted with the number of population iteration and individual fitness value, letting the algorithm exploration and development more reasonable. The experimental results implicate that the IADE algorithm has better performance, higher convergence and efficiency to solve the multitasking problem compared with other algorithms.

单机多任务处理在多架无人机协同控制中发挥着重要作用，是最复杂、最难的部分。本文建立了三维地形图，提出了一种改进的自适应差分进化（IADE）算法，用于单无人机多任务处理。作为一个优化问题，使用标准差分进化来获得全局最优解的效率很低，可以避免这个问题。因此，该算法将变异因子和交叉因子引入到动态自适应函数中，使得交叉因子和变异因子可以随着种群迭代次数和个体适应度值进行调整，使算法的探索和开发更加合理。