基于信息素启发狼群算法的UAV集群火力分配

无人机（UAV）集群作战是未来智能化战争的重要作战样式。为充分发挥UAV集群整体作战优势,得到最优武器-目标分配（WTA）方案,使得UAV集群在火力分配中既能够满足任务要求,又能够较少作战单元消耗,建立了包含任务完成、有效杀伤、攻击消耗约束的UAV集群火力分配数学模型,采用带有游走、召唤算子的改进狼群算法（WPA）对模型进行求解。为提高算法全局寻优效率,避免陷入局部最优,引入蚁群优化（ACO）算法中信息素启发规则,对游走行为及狼群更新机制进一步改进,提出了基于信息素启发狼群算法（PHWPA）的UAV集群进攻的火力分配方法。仿真结果表明:所提方法是有效的,相比较于其他算法,PHWPA具有更高效的寻优能力,能够为UAV集群作战火力规划提供支持。 

Weapon-target assignment in UAV cluster based on pheromone heuristic wolf pack algorithm

Unmanned Aerial Vehicle (UAV) cluster operation is an important mode of intelligent warfare in the future. In order to give full play of the overall operational advantages of UAV cluster, a mathematical model is constructed to solve the Weapon-Target Assignment (WTA) problem in UAV cluster attacks and obtain the optimal scheme. The constraints of mission completion, effective killing and attack consumption are established in the model, which can meet the requirements of the mission, and also save the consumption of UAV combat units to maintain the power of UAV cluster. The improved Wolf Pack Algorithm (WPA) with scouting and summoning operators is used to solve the model. To obtain the higher global optimization efficiency and avoid trapping in local optimum, the weapon-target assignment in UAV cluster attack based on Pheromone Heuristic Wolf Pack Algorithm (PHWPA) is proposed to improve WPA's scouting behavior and renewable mechanism by using pheromone heuristic rules from Ant Colony Optimization (ACO). The simulation results show that the proposed method is effective. Compared with several algorithms, PHWPA has more efficient search ability. The proposed method can provide support for firepower planning of UAV cluster.