Fuzzy Clustering Algorithm Based on Hybrid Artificial Bee Colony
YAO Ya
GAO Shang
Abstract：Making full use of the global statistical information obtained by the distributed estimation algorithm,this paper im?proves a new method of generating nectar in the artificial bee colony algorithm,and proposes an artificial bee colony algorithm based on continuous distribution estimation to effectively improve the global exploration ability of artificial bee colony algorithm.Because the selection of initial clustering centers has a great impact on the clustering effect of fuzzy C- means clustering algorithm and this al?gorithm is easy to fall into local optimum,a fuzzy clustering algorithm based on hybrid artificial bee colony algorithm is put forward—which combined the fuzzy clustering algorithm with the improved artificial bee colony algorithm to improve the clustering effect. The main idea of the algorithm is to use the hybrid artificial bee colony algorithm to obtain the initial cluster center,and then the FCM al?gorithm is used to optimize the initial clustering center,and finally the global optimum is gotten. The experiment shows that the con?vergence rate of the improved artificial bee colony algorithm is faster than the traditional artificial bee colony algorithm,and the new FCM algorithm has better clustering effect,and improves the accuracy and convergence speed.
Keywords：artificial bee colonyfuzzy C-meansestimation of distribution algorithmintelligent hybrid algorithmUMDAc
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1072-1077 )