Research on Rural E-Commerce Logistics Planning Based on Ant Colony Algorithm

The ant colony algorithm has been widely applied in the field of logistics path planning. However, in practical applications, it faces problems such as slow convergence speed and a tendency to get trapped in local optimal solutions. Especially in large-scale logistics networks with numerous nodes, it takes a long time for the algorithm to find a relatively good path, and it cannot guarantee to obtain the global optimal solution, which limits its application effect in complex logistics scenarios. To address these issues, during the path selection process, the heuristic information is improved by taking into account the distance factor between the selected point and the destination, and an adaptive direction adjustment factor is introduced. This enables the algorithm to expand its search range in the early stage, enhance its random search ability and the number of effective solutions in the later stage. As a result, it can better adapt to complex environments in path planning and increase the probability of finding the optimal path.