基于改进粒子群优化的物流运输车路径规划方法

Logistics transport vehicle path planning based on improved particle swarm optimization

物流运输车路径规划问题是一个复杂的组合优化问题,因此,文章提出了一种基于改进粒子群优化算法的物流运输车路径规划方法,对粒子群优化算法中的惯性权值、学习因子和随机数进行了改进,并在算法的优化过程中引入了Levy flight模型,以避免过早的粒子群优化。并将该方法与蚁群算法和遗传算法进行了实验对比。实验结果表明,该方法能够有效降低了运输车的路径距离,显著提高物流运输的效率,降低了运输成本。

The logistics transportation vehicle path planning problem is a complex combinatorial optimization problem.Therefore,this paper proposes a logistics transport vehicle path planning method based on an improved particle swarm optimization algorithm.The inertia weight,learning factor and random number in the particle swarm algorithm are improved,and the Levy flight model is introduced in the optimization process of the algorithm to avoid premature particle swarm optimization.And the method is experimentally compared with ant colony algorithm and genetic algorithm.The experimental results show that the method can effectively reduce the path distance of transportation vehicles,significantly improve the efficiency of logistics transportation,and reduce transportation costs.