基于可射击概率约束的防空作战火力优化分配

针对防空作战火力优化分配中未考虑可射击概率影响防空作战效能的问题，提出一种基于可射击概率约束的火力分配模型。该模型综合考虑可射击概率、空袭强度、火力单元转火时间等多种因素，能够在保证满足可射击概率和联合毁伤概率阈值前提下，优先使用反应快的火力单元拦截飞临时间短的目标，并尽量减少火力资源消耗，为防空系统提供持续作战能力。提出一种基于非线性自适应惯性权重的改进平衡优化器优化算法，对模型进行求解。该算法首先使用Tent混沌映射初始化种群，增强种群多样性；其次引入惯性权重平衡局部搜索和全局搜索能力，有效提高了算法的寻优能力。仿真计算结果验证了所提模型的优点以及优化算法的有效性。

A Weapon-target Assignment in Air-defense Operations Based on Shooting Probability Constraint

A novel weapon-target assignment model based on shooting probability constraint is proposed in which the impact of shooting probability on the effectiveness of air-defense operations is considered. The proposed model takes many factors into account，such as shooting probability，air strikes intensity，and fire transfer time of firepower unit. The proposed can give priority to using firepower units with quick response to intercept the targets with short flying time on the premise of meeting the thresholds of shooting probability and joint damage probability. At the same time，it minimizes the consumption of fire resources to provide continuous combat capability for the air-defense system.On this basis，an improved equilibrium optimizer algorithm based on nonlinear adaptive inertia weight is presented to solve the weapon-target assignment problem. The Tent chaotic map is used to generate the initial population to enhance the diversity of the population. And the inertia weight is introduced to balance local search and global search ability，which effectively improves the optimization ability of the algorithm. Simulated results verify the advantages of the proposed model and the effectiveness of the optimization algorithm.