针对异构多无人机任务分配不合理、速度慢的问题，提出一种基于改进多元宇宙优化(multi-verse optimization, MVO)算法的多异构无人机任务分配方法。考虑无人机特性和目标属性的关系，建立任务分配目标函数，并采用 Logistics 混沌初始化和差分变异算子改进 MVO 算法，对该目标函数进行求解，避免 MVO 算法陷入局部最优并加快其收敛速度，使多无人机任务分配以较小的代价得到较大的收益。仿真结果表明，与 MVO 算法、 遗传算法和粒子群优化算法相比，改进 MVO 算法能更有效地解决多无人机任务分配问题。

For the problems of unreasonable task assignment and slow speed of heterogeneous multiple unmanned aerial vehicles (UVA), a task assignment method based on improved multi-verse optimization (MVO) algorithm is proposed. In this method, the relationship between UAV characteristics and target attributes is considered to establish the objective function of task assignment, and Logistics chaos initialization and differential mutation operator are used to improve MVO algorithm to solve the objective function, so as to avoid the MVO algorithm falling into local optimum and accelerate its convergence rate. Task assignment of multi-UAV can gain more benefits at less cost. By comparing with MVO algorithm and particle swarm optimization algorithm, the simulation results show that the improved MVO algorithm is more effective to solve the task assignment problem of multi-UAV.