A New Prediction Algorithm for Perception Results of Mobile Games Users

The research of prediction algorithms for perception results of mobile games users is of great significance for network operators to find network problems and improve network quality. In the prediction process, optimization is of great significance fort the algorithm to improve prediction accuracy. In order to solve the shortcomings of prediction algorithms for perception results of mobile games users, an improved Harris Hawks Optimizer optimizing the BP neural network model is proposed. Initialize population by Bernoulli chaotic map; The nonlinear energy factor is adopted to better coordinate the global search and the local development; An improved Laplace operator is proposed to enhance the capability to look for the optimal solution. Through the test of 8 benchmark test functions, results show the improved Harris Hawks Optimizer has better convergence speed and higher precision than the other four algorithms. According to ITU-T standard, a mobile games users perception data set is established. Based on this data set, experiments verify that the proposed algorithm has better prediction ability and is suitable to predict perception results of mobile games users.