基于蚁群算法的冷链物流配送路径优化研究与应用
Research and Application of Cold Chain Logistics Distribution Path Optimization Based on Ant Colony Algorithm

本研究针对旅行商问题的高效求解，探讨了传统算法的局限性，并强调了启发式算法的重要性。我们选取蚁群算法作为研究对象，因其具备自适应性和正反馈机制。然而，ACO在实际应用中常陷入局部最优解的问题，为此我们引入模拟退火算法以增强全局搜索能力，构建了一种新型混合算法。实验结果表明，混合算法在多次实验中均稳定找到全局最优解，路径长度为41.59个单位距离，验证了其有效性和可靠性。适应度曲线观察显示，即使出现异常值，模拟退火算法在局部搜索中的作用确保了全局最优解的稳定性。此外，该算法在运输问题中的应用显著降低了成本，提升了效率，减少了车辆使用时间和燃料消耗，展示了显著的优化优势。
This study addresses the efficient solution of travel quotient problems, explores the limitations of traditional algorithms, and highlights the importance of heuristic algorithms. We chose the ant colony algorithm as the research object because of its adaptability and positive feedback mechanism. However, ACO often falls into the local optimal solution in practice, so we introduce the simulated annealing algorithm to enhance the global search capability, and build a new hybrid algorithm. The experimental results show that the hybrid algorithm stably finds the global optimal solution in many experiments with a path length of 41.59 unit distances, which verifies its validity and reliability. The fitness curve observations show that the role of the simulated annealing algorithm in the local search ensures the stability of the global optimal solution even with outliers. Moreover, the application of the algorithm in transportation problems significantly reduces cost, improves efficiency, and reduces vehicle usage time and fuel consumption, demonstrating significant optimization advantages.