摘要
防空导弹飞行试验后弹目残骸有着重要价值。根据残骸搜索的实际需求,把残骸落点纳入到路网中,结合自适应路由算法,改进了基本蚁群算法,解决了靶场残骸搜索的最优路径问题。蚁群算法有收敛性较差、易于过早陷入局部最优等不足,通过构建蚁群、引入信息素约束条件、调整信息素初始值、自适应改变信息素增量等技术,增强了蚁群搜索能力,改善了算法收敛速度。仿真表明该算法易于编程实现,时延小,鲁棒性强,实用性好。
Wreckage of antiaircraft missile and target after flying test has an important value. According to the re- quirement of wreckage searching, a new method is proposed and applied to find the optimal path joining falling point in path net in this paper. It can improve the classical ant colony optimization algorithm（ACO） by combining adaptive dynamic routing algorithm. ACO has worse performance in convergence and is easy to fall into the local maximum. The method enhances ACO convergence and searching ability by constructing ant colony, importing pheromone restriction, adjusting pheromone initial value, adaptive updating pheromone value. Simulation results show that the method has excellence in programming realization, time delay, robustness and practicability.
出处
《计算机仿真》
CSCD
北大核心
2009年第3期55-57,共3页
Computer Simulation
关键词
蚁群算法
自适应动态路由算法
最优路径
信息素
Ant colony algorithm
Adaptive dynamic routing algorithm
Optimal path
Pheromone
