UCAV协同攻击多目标的任务分配技术研究

Research on Task Allocation for UCAVs Cooperatively Attacking Multiple Targets

为解决单目标函数构建的任务分配模型不能给火控决策者提供更多有用信息的问题,将无人机(UCAV:Unmanned Combat Aerial Vehicle)损耗代价和目标毁伤价值作为UCAV协同攻击任务分配的两个目标函数,对其进行多目标优化,建立新型任务分配模型。在此基础上,采用一种改进带精英策略的快速非支配排序遗传算法(NSGA-II:Nondominated Sorting Genetic Algorithm II)进行求解,得到多目标协同攻击任务分配的Pareto最优解集,然后根据决策者的偏好选取最佳的任务分配方案。最后通过仿真算例,验证了该算法的收敛性及有效性。

Task allocation model based on single objective function can not provide more useful information for the fire-control decision makers.In order to make up the deficiency,the wastage cost of UCAV（Unmanned Combat Aerial Vehicle） and damage value of target are treated as two optimization objective functions of the task allocation for Multi-UCAV cooperatively attacking multiple targets,and a new task allocation model is established.Based on the optimization model,an improved NSGA-Ⅱ（Nondominated Sorting Genetic Algorithm Ⅱ） with elitist strategy is adopted for searching the Pareto optimal solutions of the task allocation of cooperative attacking multiple targets for Multi-UCAV.The decision makers select the best task allocation scheme according to their preferences.Simulation results demonstrate that the algorithm of task allocation is convergent and effective.