Research On The Ant Colony Algorithm Based On Parameter Control Of Open Vehicle Routing Problem

The rapid development of Internet and computer technology has driven the development of e-commerce.As an important support of E-commerce,logistics distribution shows the extreme importance in social life.The vehicle routing problem is the foundation and key work of logistics distribution.The important part of the logistics distribution is how to arrange reasonable vehicle routing scheme to make the total travel distance shortest,spend a minimum of time,and the lowest transportation cost,which has important practical significance and economic significance.In this paper,an improved ant colony algorithm with parameter control is put forward to optimize the open vehicle routing problem.Meanwhile,considering the traditional logistics distribution system has been unable to meet the needs of contemporary social logistics distribution,the author made further expansion of open vehicle routing,considered the factors of customer time windows and cross-regional coordination transport among distribution centers,and studied multi-depot open vehicle routing problem with soft time windows in this paper.As a kind of swarm intelligence algorithm,ant colony algorithm is often applied to the optimal routing problem.The parameters of the most traditional ant colony algorithm use a fixed value,and parameters in the algorithm have an important impact on the performance of ant colony algorithm.Therefore,the convergence speed of traditional ant colony algorithm is slow and it is easy to fall into partial optimum.In this paper,by analyzing information of elicitation factor parameter ?,expected elicitation factor ? and pheromone evaporation coefficient ?,the author further studied the impact on the scheduling performance of the parameters in the vehicle routing model,and proposed vehicle routing approaches based on the parameters dynamic adaptive optimization.At first,the author verified the effectiveness of the improved ant colony algorithm in the single-depot open vehicle routing problem with soft time windows,and then applied it to the multi-depot open vehicle routing problem.The innovations and research results of this paper mainly include the following:Firstly,the author studied the multi-depot open vehicle routing problem with soft time windows based on the basic open vehicle routing problem.To solve this problem,the author set up a virtual depot,the multi-depot VRP was transformed into a single depot VRP.Secondly,at the early stage of ant colony algorithm,a clustering technology based on the bacterial foraging chemotaxis algorithm and K-means algorithm were used to judge the state of the ant colony,and the parameters were adjusted adaptively to make the algorithm converge to the neighborhood of the global optimal solution.Finally,at the late stage,the parameters were tuned based on the ergodicity of Chaos Theory to jump out of partial optima.After the algorithm,the author adopted 2-opt algorithm to optimize the optimal solution.This paper made a useful exploration on the construction of the vehicle routing problem model and the algorithm,which has important theoretical significance on developing new vehicle routing approaches,and has important practical significance on improving the service level of the logistics and reducing logistics costs.