For promoting the prediction accuracy of network traffic,a prediction model based on improved slime mold algorithm and optimized support vector regression combined with phases space reconstruction was proposed.To solve the problems of slow convergence and easiness to fall into local optimization of slime mold algorithm,three forms of opposite learning were introduced to initialize the population and improve the diversity of the population.A nonlinear feedback factor updating was introduced to balance the global search and local development.The Cauchy-Gaussian mixture mutation was designed to perturb the optimal solution and expand the search space to avoid the algorithm falling into local optimization.The improved slime mold algorithm was used to optimize the factors of support vector regression,which improved the learning accuracy and convergence speed by effectively solving the defects that the traditional parameter adjust method in the support vector regression is easy to get the mini-mum solutions and it is sensitive to initial parameters values,and a new network traffic prediction model was constructed.The results show that the improved model has less prediction error,and it can meet the high accuracy and real-time requirements.
维普
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