An Improved Fuzzy C-Means Clustering Algorithm Based on Multi-chain Quantum Bee Colony Optimization

作     者：Feng, Yufang Lu, Houqing Xie, Wenbin Yin, Hong Bai, Jingbo 

作者机构：Army Engn Univ PLA Nanjing Jiangsu Peoples R China 

出 版 物：《WIRELESS PERSONAL COMMUNICATIONS》 (无线个人通信)

年 卷 期：2018年第102卷第2期

页      面：1421-1441页

核心收录：

学科分类：0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：National Natural Science Foundation of China 

主　　题：Fuzzy c-means Artificial bee colony algorithm Expansion of multi-chain coding Gene chain Quantum bee colony algorithm 

摘      要：The fuzzy c-means (FCM) algorithm is the most popular clustering method. Many studies of FCM had been done. However, the FCM algorithm and its studies are usually affected by the selection of initial values and noise data, and can easily fall into local optimal value. To overcome these drawbacks of FCM, this paper proposed the algorithm of FCM based on multi-chain quantum bee colony algorithm (MQBC-FCM). In MQBC-FCM, first, the multiple chains encoding method is introduced to the artificial bee colony algorithm to propose the MQBC algorithm. Then MQBC is used to search for the optimal initial clustering centers. The proposed algorithm is used on artificial data sets and image segmentations, and its performance is contrasted with several algorithms. The experimental results have indicated that the proposed MQBC-FCM has efficiently improved the performance of the clustering algorithm.