An Optimization Algorithm Based on Levy’s Flight Improvement

Aiming at the difficulties of automatic parameter optimization encountered in the development of network traffic forecasting systems, this paper, combined with recent research results of enhanced learning and evolutionary computing, A set of schemes based on improved Q-Learning strategy and Levy’s Flight combined with lightning optimization algorithm are proposed. Automatically search for optimal parameters in the data preprocessing stage of network traffic prediction and the deep learning model training stage. An optimization algorithm for the lightning attachment process based on Levy’s Flight improvement (Levy-LAPO) is proposed. Through the overall driving ability of Levy’s Flight, solved the problem of slow convergence. This paper compares the improved algorithm with the classic algorithm on standard functions and real data sets to verify the superiority of the improved algorithm.