基于改进 ACO 算法的无人作战飞机航路规划设计

摘要

为了使无人作战飞机能在敌方防区内以最小的发现概率和航路代价到达目标点，提出一种基于改进蚁群算法对 UCAV 进行航路规划的方法；首先，根据燃油代价和威胁代价设计了目标函数，并根据敌方防区威胁源生成 VORONOI图，然后通过对信息素初始化方式、蚂蚁转移规则以及信息素更新规则进行改进以优化传统的 ACO 算法，最后定义了使用改进 ACO 算法对 UCAV 进行航路规划的具体算法；仿真实验证明文中方法能实现 UCAV 航路规划，且与经典 ACO 算法相比，文中方法具有较快的收敛速度和较强的全局寻优能力。%For making UCAV (Unmanned Combat Aerial Vehicle)reaching the goal in the enemy district with the minimum detection probability and path planning,a method based on improved ACO (Ant Colony Optimization)algorism was proposed.Firstly,the goal func-tion was designed according to the threat cost and fuel cost,and the VORONOI diagram was generated by threat source in enemy district. Then the pheromone initialization,transferring rules for ant and the renew rules for path were improved to optimize the traditional ACO algo-rism.Finally,the improved ACO algorism for planning the path for UCAV was defined.The simulation experiment shows our method can realize the path planning,and compared with the traditional ACO algorism,it has the advantages of rapid convergence and global optimizing ability.