针对多无人机对地协同攻击多任务分配问题,通过合理假设对问题进行抽象简化的基础上,建立了基于任务分配收益和代价的总体分配效能函数模型,并以此模型作为任务分配方案的评估标准。引入各种操作符重新定义了离散粒子群优化算法的速度以及位置更新公式,建立了算法实现的基本流程,并利用该DPSO优化算法对任务分配模型进行求解。分别针对多UAV单任务,单UAV多任务以及多UAV多任务进行仿真分析,结果表明,DPSO算法比遗传算法具有更好的全局搜索能力和收敛速度,通过仿真验证了任务分配模型的合理性和正确性,验证了算法的有效性和相对于遗传算法的优越性。 Aiming at the multi-task assignment problem of multi-UAV-to-ground collaborative attack, based on the assumption of abstract abstraction of the problem, an overall distributional efficiency function model based on the benefits and the cost of task assignment is established and used as the task assignment scheme Evaluation Criteria. Introducing operators to redefine the speed and position update of discrete particle swarm optimization algorithm, the basic flow of the algorithm is established, and the task assignment model is solved by the DPSO optimization algorithm. The simulation results of multi-UAV single-mission, single-UAV multi-mission and multi-UAV multi-mission respectively show that the DPSO algorithm has better global search ability and convergence rate than the GA, and the rationality of the task assignment model Correctness, verify the effectiveness of the algorithm and the superiority relative to the genetic algorithm.