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.
出处
《火力与指挥控制》
CSCD
北大核心
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)
