Optimization by Hybrid/Combined Artificial Intelligent Models


  • Wei-Chiang Hong Asia Eastern University of Science and Technology, Taiwan, China




[1] Fan, G.F., Yu, M., Dong, S.Q., et al., 2021. Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling. Utilities Policy. 73, 101294.

[2] Fan, G.F., Zhang, L.Z., Yu, M., et al., 2022. Applications of Random forest in multivariable response surface for short-term load forecasting. International Journal of Electrical Power & Energy Systems. 139, 108073.

[3] Fan, G.F., Peng, L.L., Hong, W.C., 2018. Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model. Applied Energy. 224, 13-33.

[4] Li, M.W., Xu, D.Y., Geng, J., et al., 2022. A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm. Nonlinear Dynamics. 71, 2447-2467.

[5] Li, M.W., Xu, D.Y., Geng, J., et al., 2022. A hybrid approach for forecasting ship motion using CNNGRU-AM and GCWOA. Applied Soft Computing. 114, 108084.

[6] Zhang, W.Y., Hong, W.C., Dong, Y., et al., 2012. Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy. 45, 850-858.

[7] Peng, L.L., Fan, G.F., Meng, Y., et al., 2021. Electric load forecasting based on wavelet transform and random forest. Advanced Theory and Simulations. 4, 2100334.

[8] Geng, J., Huang, M.L., Li, M.W., et al., 2015. Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model. Neurocomputing. 151, 1362-1373.

[9] Hong, W.C., Dong, Y., Zhang, W.Y., et al., 2013. Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm. International Journal of Electrical Power & Energy Systems. 44, 604-614.

[10] Ju, F.Y., Hong, W.C., 2013. Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting. Applied Mathematical Modelling. 37, 9643-9651.

[11] Song, J., Zhang, L., Jiang, Q., et al., 2022. Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model. Applied Energy. 309, 118444.

[12] Zhang, W., Lin, Z., Liu, X., 2022. Short-term offshore wind power forecasting - A hybrid model based on discrete wavelet transform (DWT), seasonal autoregressive integrated moving average (SARIMA), and deep-learning-based long short-term memory (LSTM). Renewable Energy. 185, 611-628.

[13] Şahin, U., Ballı, S., Chen, Y., 2021. Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods. Applied Energy. 302, 117540.

[14] Hong, W.C., 2011. Electric load forecasting by seasonal recurrent support vector regression (SVR) with chaotic artificial bee colony algorithm. Energy. 36, 5568-5578.

[15] Chi, D., 2022. Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model. Energy Reports. 8, 220- 228.

[16] Chung, W.H., Gu, Y.H., Yoo, S.J., 2022. District heater load forecasting based on machine learning and parallel CNN-LSTM attention. Energy. 246, 123350.

[17] Karijadi, I., Chou, S.Y., 2022. A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction. Energy and Buildings. 111908.

[18] Li, M., Hong, W.C., Kang, H., 2013. Urban traffic flow forecasting using Gauss-SVR with cat mapping, cloud model and PSO hybrid algorithm. Neurocomputing. 99, 230-240.

[19] Huang, M.L., 2016. Hybridization of chaotic quantum particle swarm optimization with SVR in electric demand forecasting. Energies. 9, 426.

[20] Peng, L.L., Fan, G.F., Huang, M.L., et al., 2016. Hybridizing DEMD and quantum PSO with SVR in electric load forecasting. Energies. 9, 221.

[21] Li, M.W., Geng, J., Wang, S., et al., 2017. Hybrid chaotic quantum bat algorithm with SVR in electric load forecasting. Energies. 10, 2180.

[22] Li, M.W., Geng, J., Hong, W.C., et al., 2018. Hybridizing chaotic and quantum mechanisms and fruit fly optimization algorithm with least squares support vector regression model in electric load forecasting. Energies. 11, 2226.

[23] Zhang, Z.C., Hong, W.C., 2019. Electric load forecasting by complete ensemble empirical model decomposition adaptive noise and support vector regression with quantum-based dragonfly algorithm. Nonlinear Dynamics. 98, 1107-1136.

[24] Li, M.W., Wang, Y.T., Geng, J., et al., 2021. Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dynamics. 103, 1167-1193.


How to Cite

Hong, W.-C. (2022). Optimization by Hybrid/Combined Artificial Intelligent Models. Journal of Management Science & Engineering Research, 5(1), 27–29. https://doi.org/10.30564/jmser.v5i1.4485


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