Abstract:

In order to improve the prediction precision of network traffic, we propose a network traffic prediction model based on optimized BP neural networks with an improved multipopulation quantum genetic algorithm. After the neural network structure is fixed, the multipopulation quantum genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network. The model divides a population into several subpopulations by using the Kmeans clustering algorithm, and maintains the diversity of the population through respective evolution of several subpopulations. Information interaction among subpopulations through immigration operation decreases the possibility of falling into local optimum. An adaptive quantum rotation gate adjustment strategy is adopted to accelerate the convergence rate. Simulation results show that compared with conventional models, the proposed model is of faster convergence rate and higher prediction precision in network traffic prediction.

Key words: network traffic prediction；quantum genetic algorithm；BP neural network；immigration operation；Kmeans clustering algorithm