Abstract
AbstractMy country is a large grain country. In recent years, with the improvement of my country’s agricultural science and technology level, grain output has become larger and larger. Grain has become a necessity for people's lives. At the same time, the development of transportation industry has also promoted grain transportation. However, in the process of grain transportation, there are often problems of transportation time and cost waste. A large number of research results show that the ant colony algorithm has a good effect in solving logistics transportation problems. Recent studies have shown that feature migration can speed up the convergence speed of ant colony algorithm. Meanwhile, matching learning can increase the diversity of ant colony algorithm solutions. This is well applied in the TSP problem model. This paper solves this problem by adding selection, crossover, and mutation operations to the ant colony algorithm. This paper first introduces the background, improvement and application of the ant colony algorithm. Then the paper introduces the CVRP model and uses the obtained data for modeling. Finally, the paper uses matlab to run the two algorithm codes and gets the experimental results. The paper evaluates the experimental results through the final shortest path and the average of all the shortest paths. Finally, compared with the traditional ant colony algorithm, the improved algorithm can speed up the convergence of the traditional ant colony algorithm. At the same time, the stability of the algorithm and the quality of the solution have also been improved. In order to improve the shortcomings of ant colony algorithm, this paper adds genetic algorithm to ant colony algorithm. Finally, we use experiments to prove that the improved algorithm has better convergence, stability, and quality of solution. This is the innovative point of this article.KeywordsGrain transportationAnt colony algorithmExperiment results
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