Research Of Improved Ant Colony Optimization For Vehicle Routing Problem

Vehicle routing problem is a NP-hard problem, solving it is a hot research of operations research, graph theory, computer simulation science and other disciplines, has made some theoretical results and application experience, and has been in the modern logistics, computer applications in areas such as widely used. Based on the VRP's model, how to find the best global optimal path for a limited time is still a hot topic of the discipline, theoretical and practical significance.Through reading literature related fields at home and abroad, analysis and summary of the proposed vehicle routing problem, development and solving method systematically introduced to clarify the background and significance of the subject. Elaborated the basic principles of ant colony algorithm, a mathematical model and algorithm flow, and analysis its parameter settings, the complexity of the algorithm,further summarizes the advantages and disadvantages of ant colony algorithm. For its easy to fall into local optimum, using the ideas of tabu search complement each other. The optimal path through the taboo table macro circumvention, expand the search space.In order to improve operating efficiency and enhance the global search capability, introduct the 2-optimization to increase the diversity Solutions.Ultimately, the realization of the CVRP solved by Matlab programming, compared to operating results of basic ant colony algorithm and improved ant colony algorithm to verify the validity of the algorithm.Experimental results show that the performance of the improved ant colony algorithm significantly with tabu search ability improved access to better global optimal solution in a limited time.