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随着遗传算法的不断发展，人们利用它来解决复杂的大规模组合优化问题。但串行遗传算法本身的缺陷和瓶颈使得它越来越天法满足人们的需要，人们开始研究遗传算法的并行化。本文在简单介绍并行遗传算法及研究现状的基础上，以TSP问题（TravelingSalesmanProblem）为实例，讨论了影响并行遗传算法性能的主要因素，并给出了相应的实验结果。
With the continuous development of genetic algorithms, people use it to solve complex large-scale combinatorial optimization problems. However, the shortcomings and bottlenecks of serial genetic algorithm make it more and more natural to meet people’s needs, people began to study the parallelization of genetic algorithms. Based on the brief introduction of parallel genetic algorithms and the research status quo, this paper discusses the main factors that affect the performance of parallel genetic algorithms (TSP) and gives the corresponding experimental results.