Network Traffic Prediction Model Combined Improved Arithmetic Optimization Algorithm and Wavelet Neural Network
Network traffic has the characteristics of non-linearity and complexity,and traditional methods have a low prediction accuracy.Therefore,a network traffic prediction model combining improved arithmetic optimization algorithm( IAOA) and wavelet neural network ( WNN) is proposed.The improved arithmetic optimization algorithm is used to optimize the initial value of key parameters of wavelet neural network,which effectively solves the defect that the conventional parameter adjustment of wavelet neural network is easy to fall into local optimization,and improves the learning accuracy and convergence speed.The optimization ability of standard arithmetic opti-mization algorithm is improved,and the Latin hypercube sampling method is designed to initialize the population and improve the popu-lation diversity.The cosine function is used to update the mathematical optimizer of AOA non-linearly for equalizing the global search and local development ability.The Gaussian mutation mechanism for the optimal solution is introduced to avoid the algorithm falling into a local optimum.Ten benchmark functions are used to simulate the optimization performance of IAOA,and it is proved that the algorithm can improve the search accuracy and convergence speed.The experimental results of network traffic prediction show that the proposed prediction model has higher accuracy and more stable prediction performance and can meet the requirements of high accuracy and real-time feature of network traffic prediction.
万方数据
维普
