Logistics transport vehicle path planning based on improved particle swarm optimization
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.
logistics transportationpath planningparticle swarm optimization algorithmLevy flight model
万方数据
维普
