Comparison between radial basis neural network improvement method with SALP optimization algorithm (RBF-SSA) with other hybrid optimization algorithms

[1] A.Z. Ala’M, A.A. Heidari, M. Habib, H. Faris, I. Aljarah and M.A. Hassonah, SALP chain-based optimization of
support vector machines and feature weighting for medical diagnostic information systems, Evolutionary Machine
Learning Techniques, (2020), 11–34.
[2] I. Aljarah, H. Faris, S. Mirjalili and N. Al-Madi, Training radial basis function networks using biogeography-based
optimizer, Neural Comput. Appl. 29 (2016), no. 7, 529–553.
[3] M. Barman, N.D. Choudhury and S. Sutradhar, A regional hybrid SSA-SVM model based on similar day approach
for short-term load forecasting in Assam, India, Energy 145 (2018), 710–720.
[4] G. Cervone, L. Clemente-Harding, S. Alessandrini and L. Delle Monache, Short-term photovoltaic power forecasting using artificial neural networks and an analog ensemble, Renew. Energy 108 (2017), 274–286.
[5] G.W. Chang, H.J. Lu, Y.R. Chang and Y.D. Lee, An improved neural network-based approach for short-term
wind speed and power forecast, Renew. Energy 105 (2017), 301–311.
[6] S. Ding, K.W. Hipel and Y.G. Dang, Forecasting China’s electricity consumption using a new grey prediction
model, Energy 149 (2018), 314–328.
[7] Y. Liang, D. Niu and W.C. Hong, Short term load forecasting based on feature extraction and improved general
regression neural network model, Energy 166 (2019), 653–663.
[8] Y. Liu, W. Wang and N. Ghadimi, Electricity load forecasting by an improved forecast engine for building level
consumers, Energy 139 (2017), 18–30.
[9] M. Meng, L. Wang and W. Shang, Decomposition and forecasting analysis of China’s household electricity consumption using three-dimensional decomposition and hybrid trend extrapolation models, Energy 165 (2018), 143–
152.
[10] S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris and S.M. Mirjalili, SALP swarm algorithm: a
bio-inspired optimizer for engineering design problems, Adv. Engin. Software 114 (2017), 163–191.
[11] M.Q. Raza, M. Nadarajah, D.Q. Hung and Z. Baharudin, An intelligent hybrid short-term load forecasting model
for smart power grids, Sustain. Cities Soc. 31 (2017), 264–275.
[12] S.R. Salkuti, Short-term electrical load forecasting using radial basis function neural networks considering weather
factors, Electr. Eng. 100 (2018), no. 3, 1985–1995.
[13] A. Tarsitano and I.L. Amerise, Short-term load forecasting using a two-stage sarimax model, Energy 133 (2017),
108–114.
[14] W. Yang, J. Wang, T. Niu and P. Du, A hybrid forecasting system based on a dual decomposition strategy and
multi-objective optimization for electricity price forecasting, Appl. Energy 235 (2019), 1205–1225.
[15] W. Yin, Y. Han, H. Zhou, M. Ma, L. Li and H. Zhu, A novel non-iterative correction method for short-term
photovoltaic power forecasting, Renew. Energy 159 (2020), 23–32.
[16] J. Zhang, Y.M. Wei, D. Li, Z. Tan and J. Zhou, Short term electricity load forecasting using a hybrid model,
Energy 158 (2018), 774–781.