基于改进蚁群算法的飞行器航迹规划

Aircraft Route Planning Based on Improved Ant Colony Algorithm

针对蚁群算法在航迹规划中易于过早陷入局部最优解这一问题,提出了一种双向自适应改进蚁群算法。使用栅格节点对飞行空间进行建模,在搜索过程中以移动方向一定范围内最大信息素和目标引导函数作为启发因子。根据蚁群算法处理该问题时的信息素散播特点,重构了信息素的更新策略和散播方式。通过信息素的震荡变化和挥发系数的自适应调整,扩大了搜索空间,提高了搜索全局性,获得了一种有效的航迹规划算法,并取得了较好的仿真结果。

The prominent problem of the ant colony algorithm in aircraft route planning is its tendency to be trapped into local optimal solution too early.An adaptive dual population ant colony algorithm is proposed to solve the problem.Using modeling information grid to describe the aircraft travel workspace,a heuristic factor based on the most pheromone in a moving direction rang and a goal guiding function are used during the searching process.Based on the features of the pheromone strewing when solving the problem by ant colony algorithm,the strewing method and updating strategy of pheromone are reconstructed.The concussion change of the pheromone and the adaptive adjustments of the volatile coefficient can expand the search space and improve the overall searching performance.The simulation result proves that the algorithm is feasible and effective in aircraft rout planning.