Fractional-Order Modeling and Fuzzy Clustering of Improved Artificial Bee Colony Algorithms

Abstract

This article proposes an improved artificial bee colony algorithm (IABC) for maintaining a balance between the exploratory and exploitative abilities of the algorithm. Therefore, unmodeled dynamic characteristics can be effectively estimated, which can improve the estimation accuracy of the system and create conditions for later system modeling and control. The proposed IABC algorithm uses two new search expressions to generate new alternative solutions and the global optimal solution is considered in the search process. Exploiting the simple structure and fast global convergence of artificial bee colony algorithm, an improved clustering algorithm combining IABC and Kernel fuzzy c-means (KFCM) iteration is also proposed. KFCM algorithm is sensitive to the initial clustering center and can easily fall into local optimum; and the improved clustering algorithm solves this issue. Compared with other algorithms, IABC algorithm can converge faster and more accurately for a given parameter structure and number of cycles. Three sets of benchmark test functions and six sets of UC Irvine standard datasets were used in the simulation experiments conducted. Experimental results show that KFCM-IABC can aggregate datasets faster than generalized fuzzy c-means (GFCM)-IABC because of the use of IABC. The proposed algorithm improves class validity index from 1 to 4%, thereby exhibiting the advantages of strong robustness and high clustering accuracy.