基于非线性规划协同进化遗传算法的防空导弹火力优化分配研究

为了进一步提高防空导弹目标分配问题的求解效率和解算能力,建立了防空导弹目标火力分配模型,提出了一种非线性规划协同进化遗传算法(NLPCGA).该算法是综合非线性规划算法(NLPA)局部搜索能力强和协同进化算法(CA)求解质量高的优点,并利用遗传理论提高算法的求解效率.通过结合实例,仿真结果表明NLPCGA算法在求解防空目标火力分配问题上要优于单独两种智能算法,可以有效快速地找到最优火力分配方案,为防空作战指挥决策提供支持.

Fire Optimal Distribution of Air-Defense Missile Based on Nonlinear Programming Co-Evolutionary Genetic Algorithm

The model of the air-defense missile fire allocation is established to improve the solving efficiency and performance,and a new nonlinear programming co-evolutionary genetic algorithm(NLPCGA) is presented.The algorithm synthesizes the advantages of the strong local search ability of nonlinear programaing algorithm(NLPA) and the high quality for solving of co-evolutionary algorithm(CA),and uses the genetic theory to improve the solving efficiency.The simulation result shows that NLPCGA is better than these two intelligent algorithms on the issue of fire distribution,and it can effectively and quickly find the optimal fire distribution scheme to provide support for air defense operation command and decision.