基于Memetic算法的超视距协同空战火力分配

针对超视距多机协同空战中,火力单元采用一次性完全分配原则容易造成资源浪费的问题,采用一种新的火力分配数学模型.该模型带有毁伤概率门限,能够保证在满足毁伤概率门限的前提下,优先保证威胁度大的目标被分配且选择对各目标杀伤概率相对较大的火力单元,使其对目标的毁伤概率平均值达到最大且尽量少地消耗火力单元,从而节省和充分利用火力资源.在此基础上,提出采用以离散粒子群算法为全局搜索策略,以贪婪算法为局部搜索策略的Memetics算法求解协同空战火力分配问题,有效地提高了算法收敛速度、精度.仿真算例验证了模型的优点及Memetic算法的有效性.

Weapon-target assignment based on Memetic optimization algorithm in beyond-visual-rang cooperative air combat

In beyond-visual-rang (BVR) cooperative air-to-air combat, weapon resources could be wasted if all weapon units are fully assigned at a time. To cope with the disadvantage, a new weapon-target assignment mathematical model based on the threshold of damage probability was proposed. The new model guarantees the threshold of damage probability by employing fewer weapon units to save and make full use of weapon resources. The proper fire units were assigned to the targets according to the priority of menace. Meanwhile, the maximum of the target damage probability average value can also be achieved. Based on the new model, a Memetic algorithm, using the discrete particle swarm optimization (DPSO) algorithm as the global search strategy and greedy algorithm as the local search strategy, was proposed to solve the weapon-target assignment problem. Simulation results show the advantage of the proposed new model and the effectiveness of Memetic algorithm.