中文摘要：

旅行商路径优化问题是经典的网络分析问题之一，主要通过智能优化方法获得近似最优解。然而，单一智能优化方法存在运算量过大、参数选择苛刻、对初值依赖性强等缺陷，很难快速实现全局优化。本文结合遗传算法的全局寻优能力和禁忌搜索的记忆功能，提出一种基于分散集中策略的遗传禁忌搜索算法，即采用遗传变异算子作为分散策略构造邻域，开辟新的搜索空间，有效提升获得全局最优解的概率；将禁忌搜索作为集中策略进行局部寻优，避免迂回探测，充分体现禁忌搜索较强的“爬山”能力，并通过实际交通网络和不同规模的节点集合，从求解精度、稳定性和效率3个方面对算法进行评价。结果表明，本文提出的交通网络旅行商路径优化的遗传禁忌搜索算法平均求解精度比禁忌搜索算法提高了9％，略优于ArcGIS；当与ArcGIS求解的TSP路径长度差异在1％以内时，禁忌搜索算法已经难以获得对应精度的TSP路径，而遗传禁忌搜索算法效率比遗传算法提高了50％，且遗传禁忌搜索算法具有很好的并行化潜力。

英文摘要：

Traveling salesman problemis a classic problemof network analysis .However ,single heuristic algorithms have some drawbacks ,such as high computational complexity ,rigorous parameters ,strong dependence on the initial value ,which are difficult to quickly achieve global optimization .This paper designed and implemented a genetic tabu search algorithmcombined with global optimization capability of genetic algorithm and the memory function of tabu search .In particul arly , genetic mutation operator exploited the new search space and enhanced the probability of obtaining the global optimal solution .Tabu search avoided circuitous detection and reflected the strong ability of mountain climbing .Moreover ,this paper evaluated the algorithmfrom accuracy ,stability and efficiency using different scale of transportation network data .The results show that genetic‐tabu search algorithmhas higher accuracy which improves 9%than tabu search algorithm when the accuracy error is less than 1% ,and it can reduce the time consump‐tion over 50% compared with genetic algorithm .