基于改进蚁群算法的物流运输路径研究

为了在复杂的交通环境中能够快速求解出物流运输的最优路径,在传统蚁群算法基础之上提出了一种基于改进蚁群算法的物流运输路径优化模型。首先,通过在传统蚁群算法中加入基于运输时间、成本、道路平均通畅程度因子的约束条件,同时改进传统信息素的更新方式,对道路上的信息素浓度进行最大最小限制,从而改变路径选择转移概率。最后,利用改进蚁群算法与CSAACO算法、ACO算法进行仿真实验,在相同实验环境条件下测试3种算法在物流运输路径的距离缩短量和时间减少量,实验数据表明,改进蚁群算法在运输距离和运输时间方面明显低于CSAACO算法和ACO算法。改进蚁群算法拥有更强的全局寻优能力,算法收敛速度更快,所需时间更少,获得的最优路径更短,提高了整个物流行业的运输效率。

Logistics transportation route research based on improved ant colony algorithm

In order to quickly solve the optimal path of logistics transportation in a complex traffic environment, on the basis of traditional ant colony algorithm, a logistics transportation path optimization model based on improved ant colony algorithm is proposed. Firstly, the model adds constraints based on transportation time, cost and average road smoothness factor to the traditional ant colony algorithm, and improves the updating method of traditional pheromone to limit the maximum and minimum pheromone concentration on the road, so as to change the transfer probability of path selection. Finally, simulation experiments are carried out on the improved ant colony algorithm, CSAACO algorithm and ACO algorithm. Under the same experimental environment, the distance and time reduction of the three algorithms are tested. The experimental data show that the improved ant colony algorithm has significantly shorter transportation distance and less transportation time than CSAACO algorithm and ACO algorithm. The improved ant colony algorithm has stronger global optimization ability, faster convergence speed, less time, and shorter optimal path, and improves the transportation efficiency of the entire logistics industry.