Optimization for Shipping Logistics Paths Based on Evolutionary Ant Colony Algorithm: Improvement and Application of Dual Population Mechanism

Abstract

To improve the efficiency of shipping logistics, reduce transportation costs, and minimize energy consumption, this study introduces a dual population mechanism to improve the conventional ant colony algorithm and applies it to optimizing shipping logistics paths. Firstly, the shipping logistics network is abstracted as a set of nodes and edges in graph theory, simplifying the complex logistics network structure and providing a framework and theoretical basis for subsequent ant colony algorithm applications. Then, objective functions are set from four aspects: minimizing logistics transportation expenses, minimizing logistics transportation time, minimizing carbon emissions, and maximizing path reliability to guide the algorithm in searching for the optimal solution. Finally, a dual population mechanism is introduced, utilizing two independent ant populations for parallel search. Population 1 adopts an elite ant strategy to achieve fast convergence, while Population 2 uses an enhanced sub path evaluation mechanism to explore new solution spaces and help the population escape from local optima. By using the path contribution evaluation mechanism, better paths can be selected to obtain the optimal shipping logistics path. According to the simulation results, it can be seen that the total transportation time of this method is 41 days throughout the entire experimental cycle, saving 9 days and 7 days respectively compared to the two existing methods; The total transportation expenses of this method is 1250000 USD, saving 170000 USD and 130000 USD respectively compared to the two existing methods; The total carbon emissions of this method are 11800 tons, saving 1700 tons and 1400 tons respectively compared to the two existing methods. It can be seen that this method outperforms existing methods in terms of total transportation time, total transportation expenses and total carbon emissions, indicating that this method effectively achieves the design expectations.