Affiliations: [a] School of Transportation and Logistics, Southwest Jiao tong University, Chengdu, Sichuan, China
| [b] Graduate School of Tangshan, Southwest Jiao tong University, Tangshan, Hebei, China
| [c] National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu, Sichuan, China
| [d] Transportation and Urban Planning Group, Eindhoven University of Technology, Eindhoven, Netherlands
Abstract: To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm’s sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability.
Keywords: Grey wolf optimization algorithm, nonlinear convergence factor, inertial step size, dynamic weight, K-harmonic means clustering, hybrid clustering
