基于粒子群算法的空战决策问题研究  

Research on Air Combat Decision-making Problems Based on Particle Swarm Optimization Algorithm

作　　者：徐振森 王建军 赵燊佳 张军[1] 王玺琛 XU Zhensen;WANG Jianjun;ZHAO Shenjia;ZHANG Jun;WANG Xichen(College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;China Academy of Launch Vehicle Technology(CALT),Beijing 100076,China)

机构地区：[1]南京航空航天大学航空学院,南京210016 [2]中国运载火箭技术研究院,北京100076

出　　处：《火力与指挥控制》2023年第7期66-73,共8页Fire Control & Command Control

基　　金：国家自然科学基金资助项目(11872209)。

摘　　要：目标分配是协同空战中的一项关键技术。针对已知威胁值和命中率的静态空战决策问题,从简化模型出发,结合目标函数和约束条件,建立空战决策模型,利用基本粒子群优化算法求解,并分别结合遗传算法、免疫算法和退火算法改进算法,改善了算法中种群的多样性,结果表明结合遗传算法改进粒子群算法速度更新算子的可行性最高,可以较好地解决空战决策问题.Target assignment is a key technology in cooperative air combat.Aiming at the static air combat decision-making problems with known threat values and hit rates,starting from the simplified model,the objective function and constraints are combined,an air combat decision-making model is established.The basic particle swarm optimization algorithm is used to solve the problems,and the genetic algorithm,immune algorithm and annealing algorithm are combined to improve the diversity of the population in the algorithm.The results show that the combination of genetic algorithm and particle swarm optimization algorithm is the most feasible to improve the speed update operators,It can better solve the air combat decision-making problems.

关 键 词：粒子群算法 算法优化 任务分配 分布协同 

分 类 号：E919[军事]