聚类问题在一定条件下可以归结为一个带约束的最优化问题,遗传算法作为一种鲁棒性很强的优化算法,可用于解决聚类问题,本文提出一种改进的混合遗传聚类算法,通过全局搜索与局部搜索相结合的方法提高收敛速度,还采用基于最近邻基因匹配的交叉算子来维持群体的多样性,实验表明,该算法的局部收敛速度和全局收敛性能均明显优于已有的几种遗传聚类算法。 Under certain conditions, the clustering problem can be reduced to a constrained optimization problem. As a robust optimization algorithm, genetic algorithm can be used to solve the clustering problem. In this paper, an improved hybrid genetic clustering algorithm , The method of combining global search and local search is used to improve the convergence speed. The crossover operator based on nearest neighbor gene matching is also adopted to maintain the population diversity. Experiments show that the local convergence speed and global convergence performance of the algorithm are obviously better than Several existing genetic clustering algorithms.