Rural Distribution Route Optimization Based On Improved Ant Colony Algorithm

At present,the last kilometer logistics distribution in rural areas is mainly completed by manpower.And because of the rugged and complex rural roads and scattered customer distribution,this distribution method will consume a lot of labor costs and time costs.Some rural areas take customers to the distribution center to pick up the way,this way will bring great inconvenience to customers.It also adds to the burden of stockpiling goods in distribution centers.Therefore,this paper adopts the way of manual cooperation with unmanned vehicles to carry out the last kilometer distribution.The distribution mode of unmanned vehicles involves automatic obstacle avoidance and path optimization.However,in the two-dimensional grid environment,the traditional ant colony algorithm has some problems such as slow convergence,easy to fall into local optimal,and many turning points in the planned path.At present,many scholars at home and abroad use fusion algorithm to solve the Vehicle Routing Problem(VRP),and have made some progress,but there are still some defects.If the solution quality is poor,it is easier to fall into local optimal during operation.Ant Colony Optimization(ACO)can explore the entire search space to find the global optimal solution.Artificial Potential Field(APF)can solve the local optimal problem:when there are multiple local minima in the environment,the artificial potential field algorithm can jump out of the local optimal point by adding random perturbations and other methods,so as to find a better solution.So the main goal of this paper is to use ant colony algorithm and artificial potential field algorithm to solve VRP.The research content is to propose an improved model of the distribution route of unmanned vehicles,and select the best solution according to the minimum cost;An improved Potential ant colony algorithm(AACO)is proposed to solve some existing problems of vehicle routing in the last kilometer of unmanned vehicle logistics distribution in rural areas.There are three innovative points in this paper:(1)Improve the pheromone update rule of the ant colony algorithm by adding an additional increment of pheromone concentration difference between the optimal path and the worst path of the previous generation into the pheromone update of the original ant colony algorithm.(2)Improve the ant colony algorithm heuristic function.In the ant colony algorithm,the artificial potential field gravity is added,and the heuristic function is modified so that the heuristic function is affected by the gravity of the target point in the artificial potential field algorithm.The potential field gravitational factor is introduced into the heuristic function.(3)Carry out secondary path optimization,connect the adjacent turning nodes of the original path generated after improvement,and determine whether the linked route passes through obstacles.If the connected path does not pass through obstacles,the excess turning points and broken lines between the two points are removed.The experimental results show that the useless turns generated by the unmanned vehicle in the course of obstacle avoidance are reduced,and the final path is the shortest and optimal.The results of obstacle avoidance experiment environment 1 show that the path length of the improved algorithm is reduced by 64.05% compared with the original ant colony algorithm and 48.90% compared with the reference document.The results of experiment environment 2show that the path length of the improved algorithm is shortened by 20.83% compared with the original ant colony algorithm,and the path length is shortened by 11.80% compared with the reference literature.