摘要
为获得满意解为目标的最优路径选择问题,给出了一种加权的LRTA (LearningReal TimeA )算法,通过改变估价函数值更新规则与解时间和解质量的相对折中,加快算法收敛速度。实例应用表明,该方法比LRTA 算法更快地收敛于满意解,是一种求解大城市稠密路网两点间最优路径的有效方法。
For obtaining a satisfactory shortest path, this paper proposed an improved LRTA* to speed up search algorithm convergence through changing value-update rules. Through the trade-off of time and quality of solution, the convergence speed was fasted. Application result shows that the method converges suboptimal solution faster than LRTA*, it is a better algorithm to solve the satisfactory solution between O-D for a big density route network.
出处
《交通运输工程学报》
EI
CSCD
2004年第1期118-120,共3页
Journal of Traffic and Transportation Engineering
基金
教育部博士点基金项目(20010497002)
关键词
智能交通
最优路径
启发式搜索算法
人工智能
值更新规则
Algorithms
Convergence of numerical methods
Heuristic methods
Mathematical models
Quality control
Real time systems
Transportation
