A Hybrid Intelligent Algorithm Based On Tabu Search And Ant Colony Algorithm To Solve VRP

Vehicle routing problem,as an important part of vehicle scheduling,is a NP difficult problem.Solving vehicle routing problem is a hot research direction in operations research,computer,graph theory,simulation and other fields.It has achieved certain theoretical and practical experience,and has been widely applied in modern logistics and computer applications.Based on vehicle routing problem model,how to find global optimal path quickly by scientific method is still a research hotspot of combination optimization problem,which has important theoretical value and practical significance.The exact algorithm for solving combinatorial optimization problems increases exponentially with the size of the problem,the advent of artificial intelligence algorithm provides a broad idea and choice for the solution of vehicle routing problem.In the existing algorithms,tabu search algorithm has been applied to solve vehicle routing problem for a long time,the potential of tabu search algorithm needs to be further explored,the main research content of this article is as follows:(1)An improved strategy is proposed for the basic tabu search algorithm with poor accuracy and efficiency.Considering the poor accuracy of tabu search algorithm,two improvements are proposed in this paper: optimizing the structure of the algorithm and enriching the neighborhood search function.The improved algorithm adjusts whether the candidate solution is superior to the optimal value in the bulletin board.Of course,the improved algorithm is modified on the basis of retaining the basic TS algorithm.The improved tabu search algorithm enriches neighborhood functions by using various neighborhood operators.Secondly,aiming at the disadvantage of tabu search algorithm,we design taboo table structure and select taboo objects reasonably.Compared with the example data of Solomon standard database,the improved tabu search algorithm and the basic algorithm have obvious improvement in both efficiency and precision.It shows that the tabu search algorithm has good theoretical value and significance.Compared with other algorithms,the results also show that the improved tabu search algorithm also has a strong advantage.(2)In many intelligent algorithms,ant colony algorithm has better global search performance,but its search efficiency is low,local search ability is not strong,but contraindication search algorithm is just the opposite,but when the tabu search algorithm is used alone,it has strong dependence on the initial solution.Therefore,it is necessary to put forward a hybrid intelligent optimization algorithm,making it complement each other and get better solution results.Since the proposed ant colony algorithm,we have made breakthroughs in both theoretical research and application.In hybrid algorithm,ant colony algorithm uses adaptive expectation heuristic factor to make pheromone reasonable iteration.In addition,in order to reduce the impatience of the tabu search to the initial solution,this text first uses ant colony algorithm to iterate a number of better solutions with certain difference degree,which is the initial solution of the improved tabu search algorithm and preserves the information of pheromone to guide the fast convergence of tabu search.The hybrid algorithm combines the large range search capability of the ant colony algorithm and the local search ability of the tabu,trying to improve the convergence performance and avoid the local optimal.Compared with the basic ant colony algorithm,tabu search algorithm and other artificial intelligence algorithms,the hybrid ant colony tabu search algorithm has been improved in precision,which shows that the tabu search algorithm and ant colony algorithm are competitive,and the gap between the known optimal solution of the standard database is narrowed,and the hybrid algorithm has the advantage in solving vehicle routing problem.