An efficient clustering method for mobile users based on hybrid PSO and ABC. (2015)

• Record Type: Journal Article
• Authors: Xu, Chong-huan
• Is Part Of: International journal of innovative computing and applications. Volume 6:Number 3/4(2015)
• Page Start: 163
• Page End: 170
• Publication Date: 2015
• Subjects: mobile users -- K-harmonic means -- KHM -- particle swarm optimisation -- PSO -- artificial bee colony -- ABC -- clustering -- mobile commerce -- m-commerce -- simulation; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

With the rapid development of mobile commerce, more and more researchers focus on mobile users segmentation. In this paper, we present an efficient clustering method which involves three sub-algorithms: K-harmonic means, particle swarm optimisation (PSO) and artificial bee colony (ABC). In order to overcome the problem of convergence to the local optimum, we use a hybrid nature-inspired algorithm, namely hybrid PSO and ABC, to solve them. In the process of evolution, the population is divided into two sub-groups. One evolves by PSO algorithm, and the other evolves by ABC algorithm. By the comparison of two fitness values generated by these different algorithms, respectively, we can get a better value. Finally, we will obtain the optimal value by iterative calculation. Detailed simulation analysis demonstrates the efficiency and effectiveness of our approach.