摘要
提出了一种基于模糊C均值算法和生物地理学优化算法的混合聚类算法(BBO-FCM).该算法结合了生物地理学优化算法的全局搜索和FCM算法快速局部搜索的特点,利用生物地理中的迁移算子来进行各解之间的信息共享,从而有效地克服了FCM对初始值敏感、易陷入局部最优等问题.将BBO-FCM算法用于图像分割,实验表明,新算法的聚类效果评价指数更好,聚类效果明显优于原始的FCM算法.
A new hybrid clustering algorithm based on biogeography-based optimization and fuzzy C means algorithm （BBO-FCM） is proposed in this paper. By incorporating the fast local search ability of FCM and the global search of biogeography-based optimization which mainly uses the biogeography-based migration operator to share the information among solutions, the algorithm eliminates FCM trapped local optimum and is sensitive to initial value. BBO-FCM algorithm is applied to image segmentation. The experimental results show that the clustering evaluation index of the new algorithm is better and the clustering effect is apparently superior to the initial FCM algorithm.
出处
《应用科技》
CAS
2012年第5期67-70,共4页
Applied Science and Technology
