Optimal path selection for logistics transportation based on an improved ant colony algorithm

The ant colony algorithm, as a heuristic intelligent optimisation algorithm, has succeeded in solving many real-world problems, such as the vehicle routing. However, the traditional ant colony algorithm has suffered from several shortcomings, including the premature stagnation and slow convergence. To address these issues, an improved ant colony algorithm is proposed in this paper. The main contribution is to adaptively adjust key parameters during the evolution. Later the proposed algorithm is validated by addressing the vehicle routing problem. Two real-world datasets are collected from two logistic enterprises separately (i.e., YUNDA and YTO) based in Huainan City, China. Comprehensive experiments have been performed by applying the proposed algorithm to search for the optimal path. Meanwhile, the comparison between the traditional ant colony algorithm and the improved algorithm has been conducted accordingly. Experimental result shows that the proposed algorithm achieves better performance in minimising routing path and reducing the computational cost.