改进蚁群算法的无人机航路规划  

Research on Path Planning for UCAV based on Improved Ant Colony Algorithm

作　　者：田伟[1] 张安[1] 

机构地区：[1]西北工业大学电子信息学院,陕西西安710072

出　　处：《火力与指挥控制》2008年第11期69-72,共4页Fire Control & Command Control

基　　金：航空基础科学基金资助项目(05053021)

摘　　要：蚁群算法是基于生物界群体启发行为的一种随机搜索寻优方法,其正反馈性和协同性使其可用于分布式系统,隐含的并行性更使其具有极强的发展潜力,在解决组合优化问题上有着良好的适应性。基于两种改进蚁群算法,分别将遗传算法的交叉操作和Dijkstra算法结合到蚁群系统的无人作战飞机航路寻优过程中,使无人作战飞机以最小的发现概率与可接受的航程到达目标点,并提高了无人作战飞机的航路寻优能力。An ant colony algorithm is a stochastic searching optimization algorithm that is based on the heuristic behavior of the biologic colony. Its positive feedback and coordination make it possible to be applied to a distributed system. It has favorable adaptability in solving combinatorial optimization and has great development potential for its connotative parallel property. This paper presents a new improved ant algorithm. The crossover operation of a genetic algorithm and the Dijkstra algorithm are used in the ant system for path optimization. This method gives a promise for the UCAV to arrive at the target with a higher survival ability and acceptable path length.

关 键 词：蚁群算法 遗传算法 DIJKSTRA算法 航路规划 无人作战飞机 

分 类 号：V279[航空宇航科学与技术—飞行器设计]