末段反TBM火力目标匹配优化及APSO求解算法

末段反导作战火力任务分配建模是一个复杂的不确定多约束问题建模，首先建立了末段双层反战术弹道导弹火力〖CD*2〗目标匹配模型，其次对传统粒子群优化算法（particle swarm optimization，PSO）进行改进给出了一种吸引子PSO（attractor PSO，APSO），APSO引入吸引子，在保持群体多样性的基础上，将粒子聚集在最优值附近，增加相应区域的粒子密度。其中，为了方便问题求解，将火力目标匹配优化任务进行分解，转化成多个子时间段,再用APSO对多个子时间段进行求解。仿真实例表明，APSO有更加优良的收敛精度尤其是收敛速度，满足了反TBM作战火力任务分配的高时效性要求。

Attractor particle swarm optimization for anti-TBM firepower-target match modeling in terminal phase

Anti-missile combat firepower task allocation modeling in terminal phase is a complex uncertain multi-constraint problem. Firstly, a two-layer anti-tactical ballistic missile (TBM) firepower-target match model is established. Secondly, the optimization algorithm named attractor particle swarm optimization(APSO) is given to solve this model. The concept of attractor is introduced, which enhances power of local search and attracts particles to gather in the best position. In order to solve the problem, the firepower target task is decomposed into many sub-periods and APSO is used to optimize such sub-periods. Experimental studies show that APSO algorithm is better in convergence accuracy especially in convergence speed, and it fulfills the demand of anti-TBM combat firepower task allocation efficiently.