Abstract
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.
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References
Yang, H.M., Pan, Z.S., Bai, W.: Review of time series prediction methods. Comp. Sci. 46(01), 21–28 (2019)
You, S.B., Yan, Y.: Stepwise regression analysis and its application. Stat. Decis. Making 14, 31–35 (2017)
Yeromenko, V., Kochan, O.: The conditional least squares method for thermocouples error modeling. In: 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), vol. 1, pp. 157–162. IEEE (2013)
Xu, H.J.: Research on global least squares analysis of autoregressive AR model. Donghua University of Technology (2012)
Rahal, R.: Moving average model for daily euro index in Europe with genetic algorithms and comparing it with box-Jenkins model. Int. J. Math. Stat. 19(2) (2018)
Ye, G.Y., Luo, Y.H., Liu, Y., et al.: Research on power system load forecasting method based on ARMA model. Inform. Technol. (06), 74–76 (2002)
Chen, X., Przystupa, K., Ye, Z., Chen, F.: Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on levy flight. Eksploatacja i Niezawodnosc-Maintenance and Reliability 24(1), 153–162 (2022)
Zou, B.X., Liu, Q.: Network traffic prediction based on ARMA model. Comput. Res. Develop. (12), 1645–1652 (2002)
Han, C., Song, S., Wang, C.H.: Real time adaptive prediction of short-term traffic flow based on ARIMA model. J. Syst. Simul. (07), 1530–1532+1535 (2004)
Peng, D.C.: Basic principle and application of Kalman filter. Softw. Guide 8(11), 32–34 (2009)
Yang, H.Z., Zhang, Y.: Comparison between box Jenkins model deviation compensation method and other identification methods. Control Theo. Appl. (02), 215–222 (2007)
Pinto, A., et al.: Combining unsupervised and supervised learning for predicting the final stroke lesion. Med. Image Anal. 69, 101888 (2021). https://doi.org/10.1016/j.media.2020.101888
Mader, W., Linke, Y., Mader, M., et al.: A numerically efficient implementation of the expectation maximization algorithm for state space models. Appl. Math. Comput. 241, 222 (2014)
Karthika, S., Sairam, N.: A Naïve Bayesian classifier for educational qualification. Indian J. Sci. Technol. 8(16) (2015)
Lailiyah, S., Hafiyusholeh, M.: PERBANDINGAN ANTARA METODE K-MEANS CLUSTERING DENGAN GATH-GEVA CLUSTERING. Mantik: Jurnal Matematika 1(2), 26 (2016)
AlSaaidah, B., Al-Nuaimy, W., Al-Hadidi, M.R., Young, I.: Zebrafish larvae classification based on decision tree model: a comparative analysis. Adv. Sci. Technol. Eng. Syst. J. 3(4), 347–353 (2018). https://doi.org/10.25046/aj030435
Chau, G., Kemper, G.: One channel subvocal speech phrases recognition using cumulative residual entropy and support vector machines. IEEE Latin Am. Trans. 13(7) (2015)
Wang, H.X., Cao, B.: Effectiveness test of China’s stock market based on genetic programming. Comp. Sci. 43(S1), 538–541 (2016)
Abraham, S.K., Sugumaran, V., Amarnath, M.: Acoustic signal based condition monitoring of gearbox using wavelets and decision tree classifier. Indian J. Sci. Technol. 9(33) (2016)
Sun, X., Young, J., Liu, J.H., et al.: Predicting pork color scores using computer vision and support vector machine technology. Meat Muscle Biol. 2(1) (2018)
Shareef, H., Ibrahim, A.A., Mutlag, A.H.: Lightning search algorithm. Appl. Soft Comput. 36, 315 (2015)
Sun, S., Przystupa, K., Wei, M., Yu, H., Ye, Z., Kochan, O.: Fast bearing fault diagnosis of rolling element using Lévy Moth-Flame optimization algorithm and Naive Bayes. Eksploatacja i Niezawodnosc – Maintenance and Reliability 22(4), 730–740 (2020)
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Wei, M., Li, Z. (2024). An Optimization Algorithm Based on Levy’s Flight Improvement. In: Hong, W., Kanaparan, G. (eds) Computer Science and Education. Computer Science and Technology. ICCSE 2023. Communications in Computer and Information Science, vol 2023. Springer, Singapore. https://doi.org/10.1007/978-981-97-0730-0_13
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DOI: https://doi.org/10.1007/978-981-97-0730-0_13
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