一种基于萤火虫群的实际流量预测方法

针对计算机网络快速发展产生的拥塞现象,在以往的研究基础上利用萤火虫群优化方法提出一种新的预测算法PGS(Prediction method based on Glowworm Swarm).该算法首先将到达流量视作萤火虫群,节点服务率视作吸引度,通过对萤火虫个体执行移动操作和随机飞行操作来获得最优位置和吸引度,以此达到提高预测精度的目的.同时,结合OPENT和MATLAB进行仿真实验,深入研究了影响PGS算法预测误差的关键因素.最后,对比分析了小波变换预测方法,本文算法的预测误差降低了1.08%,结果表明PGS具有较好的适应性.

An actual traffic prediction method based on glowworm swarm

In order to mitigate congestion caused by the rapid growth of computer network, a novel traffic prediction algorithm PGS (Prediction method based on Glowworm Swarm) is proposed by glowworm swarm optimization method. In this algorithm, the arrival flow is regarded as glowworm swarm and the node service rate is regarded as attractiveness at firs, and in order to improving the prediction accuracy, the optimal position and attractiveness is obtained with the individuals moving operation and random flying operations. Then, a simulation with OPENT and MATLAB was conducted to research on the key factors of prediction error for PGS. Compared to Wavelet Transform prediction method, the prediction error is decreased 1.08%. The result shows that PGS has better adaptability