Experimental study on solving Traveling Salesman Problem (TSP) with genetic algorithm
遗传算法求解旅行商问题（TSP）的实验研究

摘要

旅行商问题（TSP）作为经典的组合优化难题，广泛应用于路径规划与物流调度等领域。传统算法在求解大规模TSP时效率较低，难以满足实际需求。本文围绕遗传算法在TSP问题中的应用展开研究，提出一种基于改进遗传算子的求解策略，通过优化交叉、变异机制和引入局部搜索策略，提升算法收敛速度与解的质量。实验结果表明，该方法在多个标准测试数据集中均取得较优路径长度，验证了其有效性与稳定性，为复杂优化问题提供了新的解决思路。

Abstract

As a classic combinatorial optimization problem, the Traveling Salesman Problem (TSP) is widely applied in fields such as path planning and logistics scheduling. Traditional algorithms exhibit low efficiency in solving large-scale TSP, making it difficult to meet practical needs. This paper focuses on the application of genetic algorithms in TSP, proposing a solution strategy based on improved genetic operators. By optimizing crossover and mutation mechanisms and introducing a local search strategy, the algorithm's convergence speed and solution quality are enhanced. Experimental results show that this method achieves superior path lengths in multiple standard test datasets, verifying its effectiveness and stability. It provides new solutions for complex optimization problems.