Application Of Bass Model And Particle Swarm Optimization On Forecasting Diffusion Of Mobile Internet

Mobile Internet is the combination of mobile communication and Internet. It has great potential and has penetrated many aspects of people’s life and work. The research of making accurate forecast of mobile Internet future diffusion trend can offer guidelines to the development of information industry. This paper proposes a method which is based on Bass innovation diffusion model. To solve the problem that the parameters of the model are difficult to estimate, the particle swarm optimization(PSO) algorithm with nonlinear changed inertia weight is applied to estimate the parameters. The results of the simulation show that the modified PSO can effectively improve the precision of the optimal solution and optimized Bass model is suitable for predicting mobile Internet diffusion trend.In order to further improve the forecasting accuracy of mobile Internet future diffusion trend. A method which is based on modified Bass innovation diffusion model in which the values of three parameters change over time is proposed. A novel particle swarm optimization algorithm is introduced to yield the most precise parameters. This algorithm employs opposition-based learning strategy to increase the diversity of population during the stage of initialization and execution. The introduction of population density helps determine the status of convergence of population. When the algorithm is trapped into local optima, the combination of Cauchy mutation and Gaussian mutation is applied on the global best particle to lead the swarm to search for the global optima. The results demonstrate better 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.