Study on network traffic forecast model of SVR optimized by GAFSA
GAFSA优化SVR的网络流量预测模型研究

There are some problems, such as low precision, on existed network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR)algorithm optimized by global artificial fish swarm algorithm(GAFSA). GAFSA constituted an improvement of artificial fish swarm algorithm, which was a swarm intelligence optimization algorithm with a significantly effect of optimization. The optimum training parameters could be calculated with optimizing by chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models, the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improves to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.