Abstract
A network traffic prediction model is built based on LSTM neural network optimized with the improved firefly algorithm (IFA-LSTM). Aiming at some disadvantages of firefly algorithm including slow convergence, and easy to fall in local optimal values, we introduce a location update strategy based on the diversity of population, to avoid the optimization to fall into local optimal values. A dynamic step length updating measure is proposed to improve accuracy of the optimization, and to avoid the optimal solutions’ oscillation problem. Simulation examples show that the prediction accuracy and convergent speed of the IFA-LSTM method are obviously improved,it can be used to predict network traffic.
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Acknowledgment
This work was supported by State Grid Technology Project ‘Edge Computing Research in Smart Grid Application and Security’ (Grant. 52110118001H); National Natural Science Foundation of China (Grant. 61702048).
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Han, X., Qi, F. (2020). Network Traffic Forecasting Using IFA-LSTM. In: Liu, Q., Mısır, M., Wang, X., Liu, W. (eds) The 8th International Conference on Computer Engineering and Networks (CENet2018). CENet2018 2018. Advances in Intelligent Systems and Computing, vol 905. Springer, Cham. https://doi.org/10.1007/978-3-030-14680-1_75
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DOI: https://doi.org/10.1007/978-3-030-14680-1_75
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