对多无人机协同多任务分配优化问题的目标函数构建进行研究。多无人机协同多任务分配模型的两个主要目标函数(无人机团队总飞行航程最小化和执行完所有任务时间最小化)都有各自的局限性，且两个指标函数存在利益冲突。通过为两个指标函数分配权重，建立融合两个指标函数的新指标函数，采用离散粒子群算法对协同多任务分配模型进行求解。通过调节权重的大小，可以适应不同的战场环境，使得两个指标函数都得到优化。仿真结果验证了所提方法的有效性。

The cost function of cooperative multi-task assignment for multiple UAVs is studied.The two main objective functions for the multi-task assignment are minimum cumulative distance travelled by all the UAVs to perform the required tasks and minimum time for the UAV team to accomplish all the required tasks.Both of them have their own limits, and there are conflicts between them.On this basis, a weighted sum method is employed, and a new cost function that combining the above two performance criterions is established.By choosing different weight values, the intention of the operator can be reflected.The discrete particle swarm optimization algorithm is applied to solve the cooperative multi-task assignment problem.Simulation results demonstrated the feasibility of the proposed cost function and algorithm.