一种改进萤火虫算法的模糊聚类方法

Fuzzy Clustering Method Based on Improved Firefly Algorithm

模糊C均值聚类容易受噪声数据影响,进而影响聚类准确率.鉴于此,提出了一种改进萤火虫算法的模糊聚类方法.该方法首先在萤火虫算法中引入Chebyshev映射初始化种群的分布;然后提出一种自适应步长方法来平衡探索与开发能力;最后在局部搜索过程中对每次迭代的最优个体加入高斯扰动策略,使其跳出局部最优.该过程拥有良好的寻优能力,易于得到全局最优值,将其作为模糊C均值聚类算法的初始中心进行聚类,可有效增强算法的鲁棒性,提高算法的全局寻优能力.为了评估算法的有效性,在4个数据集上进行了对比实验,实验结果表明该算法在聚类准确率和鲁棒性方面均优于对比算法.

Fuzzy C-means clustering is easily affected by noise data,which reduces the accuracy of clustering.In view of this,a fuzzy clustering method based on the improved firefly algorithm is proposed.This method firstly introduces Chebyshev mapping in the firefly algorithm to initialize the population distribution;then proposes an adaptive step size method to balance the exploration and exploitation capabilities;Finally,Gaussian perturbation is added to the optimal individual of each iteration in the local search process,and the local optimum is jumped out.This process has a good optimization ability,and it is easy to obtain the global optimal value.By taking it as the initial center of the fuzzy C-means clustering algorithm for clustering,the robustness of the algorithm can be effectively enhanced and the global optimization ability of the algorithm can be improved.In order to evaluate the effectiveness of the algorithm,comparative experiments were carried out on four data sets.The experimental results show that the algorithm is superior to the comparison algorithm in terms of clustering accuracy and robustness.