摘要
现有的交通信息系统都是基于最短路径等单一目标而建设的,这种单一目标已经不足以满足新时代下用户的交通需求。本文利用蚁群算法求解交通诱导模型的非劣解,同时针对交通空间数据的位置相关性,对蚁群算法进行基于探索范围的改进,改进的蚁群算法通过降低备选行驶节点中位置相关性低的节点的选择概率,加快蚂蚁寻求最优解的速度。
The existing traffic information systems are based on the single objective,such as the shortest path,which is insufficient to meet the new era of traffic demand.This paper makes use of ant colony algorithm to solve non-inferior solution of the traffic-induced problems.This paper not only uses the traffic-induced model of ant colony algorithm for solving non-inferior solutions,but also improves the algorithm based on explores range for the position correlation of traffic spatial data.The improved algorithm reduces the probability when the ant colony algorithm chooses the little relativity position.And therefore,it accelerates the search speed for optimal solution.
出处
《计算机与现代化》
2011年第11期27-31,共5页
Computer and Modernization
关键词
蚁群算法
最短路径
多目标优化
交通诱导
ant colony algorithm
shortest path
multi-objective
traffic-induced
