基于优化蚁群算法的动态路径规划问题研究

A dynamic path planning model based on the optimal ant colony algorithm

为解决传统的交通流分配模型在处理突发的交通事故、交通拥堵等实时交通信息时,无法对其作出合理及时的处理的问题。将动态交通网中实时变化的交通信息加入到路径规划模型当中,构建了基于优化蚁群算法的动态路径规划模型,通过与传统动态路径规划模型的比较,证明该模型收敛速度更快,且能有效避免局部收敛现象,实现了交通网络中车流量的合理分配,在大规模动态交通网络中表现更为明显。

The traditional traffic assignment model can not solve the problem of traffic accidents, traffic congestion and other real-time traffic information reasonably and timely. In order to solve these problems, real-time changes in road network information are added to the dynamic traffic as- signment model, a dynamic module for dynamic traffic assignment model based on the optimal ant colony algorithms. Compared with the traditional dynamic path planning model, the dynamic traffic assignment model converges faster and can effectively avoid local convergence. A reasonable distri- bution of traffic flow in traffic network is achieved and it is more effective in the large-scale dynamic traffic network.