Modified Grey Wolf Optimizer based Maximum Entropy Clustering Algorithm
Jia Cai, Guanglong Xu, Wenwen Ye · 2020
In this paper, we propose a new maximum entropy clustering algorithm by modified grey wolf optimizer (GWO), which modify the traditional GWO from twofold: First, nonlinear decay factor is constructed, which leads to flexible refined search; Second, proportional weights, which render the positions of distinct grey wolves, are adjusted adaptively according to the social hierarchy of them. Based on these two modifications, the rectified GWO could greatly improve both the accuracy and convergence rate. Experimental results on 12 benchmark functions demonstrate the effectiveness of those modifications. Furthermore, we utilize modified GWO to maximum entropy based fuzzy clustering problems. Experiments on 5 real datasets indicate the high performance and efficiency of the proposed approach.