Logistics Routing Intelligence based on Improved Ant Colony Algorithm and Dijkstra Algorithm

As the domestic logistics industry still faces challenges in terms of efficiency and cost-effectiveness, the optimization of logistics routes is crucial to address these challenges. The purpose of this study is to explore the potential of using ant colony algorithm and route optimization algorithm to realize intelligent optimization of logistics routes. It first introduces the principle of ant colony algorithm and its application in path optimization, and then proposes a path intelligent optimization method based on ant colony algorithm by introducing pheromone concentration and heuristic information, which improves the efficiency and accuracy of the algorithm. Finally, the experimental results show that the improved ant colony algorithm can better plan the path than the traditional ant colony algorithm, and can improve the convergence speed of calculation, and can achieve higher optimization accuracy and calculation efficiency, so that it is suitable for the practical application in the field of logistics path intelligent optimization.