基于Levy飞行的萤火虫模糊聚类算法

Firefly fuzzy clustering algorithm based on Levy flight

针对模糊C均值(FCM)聚类算法易受初始聚类中心影响而陷入局部最优问题,提出了一种基于Levy飞行的萤火虫模糊聚类算法(LFAFCM)。该算法改变萤火虫算法的随机移动策略,以平衡算法局部搜索和全局搜索能力;萤火虫位置更新过程中引入Levy飞行机制,以提高全局寻优能力;根据迭代次数和萤火虫位置动态调整每个萤火虫的尺度系数,以限制Levy飞行可搜索范围,并加快算法收敛速度。利用5个UCI数据集对算法进行实验验证,实验结果表明,该算法有效避免了陷入局部最优并具有较快的收敛速度。

Fuzzy C-Means(FCM)clustering algorithm is sensitive to the initial clustering center and is easy to fall into local optimum.Therefore,a Firefly Fuzzy C-Means clustering Algorithm based on Levy flight(LFAFCM)was proposed.In LFAFCM,the random movement strategy of firefly algorithm was changed to balance the algorithm s local search and global search capabilities,the Levy flight mechanism was introduced during the firefly position update process to improve the global optimization ability,and the scale coefficient of each firefly was dynamically adjusted according to the number of iterations and the firefly position to limit the searchable range of Levy flight and speed up the convergence of the algorithm.The algorithm was validated by using five UCI datasets.The experimental results show that the algorithm avoids the local optimum and has a fast convergence speed.