基于MODPSO-GSA的协同空战武器目标分配

提出基于多目标决策理论的协同空战武器目标分配模型,并用进化多目标优化算法求解.空战是一个多阶段攻防过程,针对多数空战武器目标分配采用一次性完全分配、不考虑火力资源消耗等不足,构建多目标决策模型,在达到毁伤门限的前提下,同时对一次攻击后使敌编队的总期望剩余威胁最小和分配导弹消耗量最小两个目标函数寻优.提出用多目标离散粒子群-引力搜索算法(MODPSO-GSA)求解分配模型,该混合进化多目标优化算法结合二者优点,具有稳定的全局搜索能力并保证收敛到Pareto前沿.该算法可求得满足毁伤门限的不同耗弹量的分配方案最优解集以供指挥员决策参考.仿真算例验证了新模型及所提出MODPSO-GSA进化多目标优化求解算法的有效性.

Cooperative weapon-target assignment based on multi-objective discrete particle swarm optimization-gravitational search algorithm in air combat

An air combat weapon-target assignment (WTA) model based on multi-objective decision theory with a hybrid evolutionary multi-objective optimization algorithm solver was proposed. Air combat is a multi-stage process of attack-defense countermeasure, existing WTA models are based on disposable fully allocated assignment without considering the missile consumption, which does not conform to the actual air combat process. The minimum of total expected remaining threats and total consumption of missiles were selected as two objectives functions of the multi-objective decision model, with the premise of reaching damage threshold. The hybrid multi-objective discrete particle swarm optimization-gravitational search algorithm (MODPSO-GSA) was proposed to handle the model, which possesses stable global search capacity and promises to converge to Pareto frontier. A Pareto optimal solution set with damage threshold met can be obtained, which offers decision reference to the commander. Simulation results verify that the model is of advantage and the proposed MODPSO-GSA is effective.