Network traffic prediction of the optimized BP neural network based on Glowworm Swarm Algorithm

In order to improve the neural network structure and parameters set methods, on the basis of Glowworm Swarm Algorithm and the BP neural network, a Glowworm Swarm Algorithm to optimize the BP neural network algorithm is proposed. The algorithm uses the Glowworm Swarm Algorithm to obtain the better initial weights and thresholds of the network, to make up for the random defects of the BP neural network in the selection of connection weights and threshold and display the mapping ability of the generalization of BP neural network, and also make the BP neural network has fast convergence and strong learning ability. Applying the algorithm to the measured network flow algorithm and compared with the BP neural network and the Glowworm Swarm Algorithm to optimize the BP neural network, the simulation results show that the algorithm has higher forecast accuracy, thus proves the feasibility and effectiveness of the algorithm in the field of the forecast.