Traditional partition clustering method has the problem of over-reliance on the initial cluster centers and the method is prone to fall into local optimum. So an improved partition clustering algorithm based on the firefly algorithm is proposed. The algorithm considers a firefly as a set of cluster centers and class cohesion is regarded as brightness of the firefly. Then find the optimal clustering center by the fireflies attracting each other. In the process of optimization, randomly distributed firefly population is used to overcome the problem of over-reliance on the initial cluster centers and adaptive step strategy is adopted to strengthen the ability to find the exact solution of the algorithm. In order to prevent the algorithm from local optimum for population concentration, the niche technology is introduced to improve the diversity of the fireflies’ population. Experimental results indicate that the algorithm is improved in clustering precision and stability compared with traditional clustering algorithm.%针对传统的划分聚类算法过度依赖初始聚类中心并容易陷入局部最优的问题，提出基于萤火虫算法的改进划分聚类算法。该算法将萤火虫个体对应于一组聚类中心的解，类簇的聚合度对应于萤火虫的亮度，通过萤火虫个体之间的相互吸引寻找聚类中心的最优解。在寻优过程中使用随机分布的萤火虫种群克服划分聚类过于依赖初始聚类中心的问题，采用自适应步长的策略加强算法寻找精确解的能力。为了避免在寻优过程中因为种群过于集中而导致算法陷入局部最优，引入小生境技术提高萤火虫的种群多样性。仿真实验结果表明，与传统聚类算法相比，该算法的聚类精度较高，稳定性较好。
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