基于蚁群算法的智能交通最优路径研究

针对车辆智能交通最优路径问题,提出一种实时规划的蚁群算法。在该算法搜索过程中加入针对具体问题的局部搜索寻优算法,在启发函数中引入搜索方向,改进信息素更新策略,限制信息素轨迹量。利用智能交通道路模型对改进算法进行比较分析。实验结果表明,改进后的蚁群算法能够有效地解决车辆实时路径诱导问题,实现车辆实时路径诱导,具有良好的收敛性和寻优性。

The Research on the Optimal Path of Intelligent Transportation Based on Ant Colony Algorithm

Aiming at the intelligent traffic optimal path ant colony algorithm convergence speed is slow and easy to fall into local optimum problem proposed an improved ant colony algorithm, in the ant colony algorithm to search in the process of join to solve the concrete problems of local search optimization algorithm. In the heuristic function introduced search party to improved pheromone update strategy, limiting pheromone quantity, the state transfer rules introducing a priori knowledge, make ant colony tendency to have high adaptive value of search space, reduce the ant colony algo-rithm in blind search path into local optimum and shorten the search time. The experimental results show that the im-proved ant colony algorithm has good convergence and optimization, and can effectively avoid the stagnation of the al-gorithm in the local optimal solution,which proved the effectiveness of the improved algorithm.