基于指数型累积毁伤律的武器目标分配问题快速寻优

Fast Optimization of Weapon Target Assignment Based on Exponential Damage Accumulation

针对联合火力打击背景,基于考虑毁伤累积效果的指数型累积毁伤律,建立了多发次武器对目标的毁伤能力量化函数,给出了相应武器目标分配问题的数学模型。为提高求解效率和性能,根据个体的适应度值,采用自适应调整交叉率和变异率策略的自适应遗传算法,解决标准遗传算法早熟、收敛速度慢的缺点。针对包括5种武器类型(共41发)和6个目标的具体实例,采用自适应遗传算法进行求解仿真,仿真结果表明:与标准遗传算法相比,自适应遗传算法能求解出更优、更收敛的近似最优解,能实现联合火力打击场景下考虑毁伤累积的武器目标分配问题的快速寻优。

Aiming at the joint strike,based on the exponential damage accumulation law,a quantitative function of the damage ability of multiple weapons to the target is established,as well as the theoretical model of weapon target assignment is presented.To improve the efficiency and performance of the solution,self-adaptive genetic algorithm(AGA) is adopted to adjust the crossover rate and mutation rate according to the fitness value of each individual,so as to solve the shortcoming of prematurity and slow convergence of standard genetic algorithm.In this paper,a specific example is given,which includes five weapon types(total:41 weapons) and 6 targets.Compared with the standard genetic algorithm,the AGA can solve the better and more convergent approximate optimal solution,and can realize the fast optimization of weapon target assignment problem considering damage accumulation in the joint fire strike.