基于模拟退火算法的旅行商路径优化
Traveler Path Optimization Based on Simulated Annealing Algorithm

旅行商问题(TSP)是一个典型的组合优化问题，在物流配送、路径规划等领域具有广泛应用。然而，由于TSP问题属于NP-hard问题，传统的精确算法难以在大规模情况下求得最优解，因此启发式算法成为求解TSP的主要方法之一。本文研究了模拟退火算法(SA)在TSP问题中的应用，详细分析了SA的基本原理、算法流程及其在TSP问题中的求解过程。通过构建城市坐标数据，本文利用模拟退火算法进行路径优化，并在不同温度控制策略下探讨了算法的收敛性。实验结果表明，SA能够有效避免局部最优，逐步收敛至高质量解，并在全局搜索能力和计算效率方面表现出较好的平衡性。最后，本文总结了SA在TSP求解中的优劣势，并探讨了未来改进方向，如结合其他智能优化算法以提升求解效率和精度。
The traveler’s problem (TSP) is a typical combinatorial optimization problem with a wide range of applications in the fields of logistics and distribution, path planning and so on. However, since the TSP problem is an NP-hard problem, traditional exact algorithms are difficult to find the optimal solution in large-scale situations, so heuristic algorithms become one of the main methods to solve the TSP. In this paper, we study the application of simulated annealing algorithm (SA) in TSP problems, and analyze the basic principle of SA, the algorithmic process and its solution process in TSP problems in detail. By constructing city coordinate data, this paper uses the simulated annealing algorithm for path optimization and explores the convergence of the algorithm under different temperature control strategies. The experimental results show that SA can effectively avoid local optimization, gradually converge to a high-quality solution, and exhibit a good balance between global search capability and computational efficiency. Finally, this paper summarizes the advantages and disadvantages of SA in TSP solving, and explores the future improvement directions, such as combining with other intelligent optimization algorithms to enhance the solving efficiency and accuracy.