Article: Application of modified particle swarm optimisation on forecasting diffusion of mobile internet Journal: International Journal of Information and Communication Technology (IJICT) 2015 Vol.7 No.1 pp.100 - 108 Abstract: In order to make accurate forecasts of mobile internet diffusion trend, this paper proposes a method which is based on modified bass innovation diffusion model in which the values of three parameters change over time. A novel particle swarm optimisation (PSO) algorithm is introduced to find the most precise parameters. This algorithm employs opposition-based learning strategy during the stage of initialisation and execution. The application of the index of population density helps determine the convergence status of population, and inertia weight is adjusted dynamically according to the value of population density. When the algorithm is trapped into local optima, the combination of Cauchy mutation and Gaussian mutation is applied on the best particle. The results demonstrate good performance of the novel algorithm on convergence accuracy and convergence velocity and the modified Bass model has the capability to forecast the diffusion of mobile internet more accurately. Inderscience Publishers - linking academia, business and industry through research

Title: Application of modified particle swarm optimisation on forecasting diffusion of mobile internet

Authors: Zhaojie Zhu; Zhenhong Jia; Xizhong Qin; Chuanling Cao; Chun Chang

Addresses: School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China ' School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China ' School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China ' China Mobile Group Xinjiang Co., Ltd., Urumqi 830063, China ' China Mobile Group Xinjiang Co., Ltd., Urumqi 830063, China

Abstract: In order to make accurate forecasts of mobile internet diffusion trend, this paper proposes a method which is based on modified bass innovation diffusion model in which the values of three parameters change over time. A novel particle swarm optimisation (PSO) algorithm is introduced to find the most precise parameters. This algorithm employs opposition-based learning strategy during the stage of initialisation and execution. The application of the index of population density helps determine the convergence status of population, and inertia weight is adjusted dynamically according to the value of population density. When the algorithm is trapped into local optima, the combination of Cauchy mutation and Gaussian mutation is applied on the best particle. The results demonstrate good performance of the novel algorithm on convergence accuracy and convergence velocity and the modified Bass model has the capability to forecast the diffusion of mobile internet more accurately.

Keywords: mobile internet diffusion; Bass model; particle swarm optimisation; modified PSO; opposition-based learning; hybrid mutation; OHPSO; diffusion forecasting.

DOI: 10.1504/IJICT.2015.066024

International Journal of Information and Communication Technology, 2015 Vol.7 No.1, pp.100 - 108

Received: 11 Mar 2014
Accepted: 19 Jun 2014
Published online: 30 Nov 2014 *

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Title: Application of modified particle swarm optimisation on forecasting diffusion of mobile internet

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