Neural Network Architecture Selection Using Particle Swarm Optimization Technique

References

• Ananthi, J., and V. Ranganathan. 2016. Multilayer perceptron weight optimization using Bee swarm algorithm for mobility prediction. IIOAB Journal 7 (9):47–63.
   Web of Science ®Google Scholar
• Bacanin, N., T. Bezdan, E. Tuba, I. Strumberger, and M. (v) Tuba. 2020. Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics. Algorithms 13 (3):67. doi:https://doi.org/10.3390/a13030067.
   Web of Science ®Google Scholar
• Devyani, B., and S. Poonam. 2018. Review on reliable pattern recognition with machine learning techniques. Fuzzy Information and Engineering 10 (3):362–77. doi:https://doi.org/10.1080/16168658.2019.1611030.
   Web of Science ®Google Scholar
• Frazier, M., C. Longo, B. S. Halpern, and S. J. Bograd. 2016. Mapping uncertainty due to missing data in the global ocean health index. PLoS ONE 11 (8):e0160377. doi:https://doi.org/10.1371/journal.pone.0160377.
   PubMed Web of Science ®Google Scholar
• Ghanou, Y., and G. Bencheikh. 2016. Architecture optimization and training for the multilayer perceptron using ant system. International Journal of Computer Science 43 (1):10.
   Google Scholar
• Guoqiang, P. Z. 2000. Neural networks for classification: A survey. IEEE Transactions on Systems, Man, and Cybernetics 30 (4):451–62. doi:https://doi.org/10.1109/5326.897072.
   Web of Science ®Google Scholar
• Hecht-Nielsen, R. 1988. Theory of the backpropagation neural network. Neural Networks 1 (Supplement–1):445–48. doi:https://doi.org/10.1016/0893-6080(88)90469-8.
   Google Scholar
• Islam, B. U., Z. Baharudin, M. Q. Raza, and P. Nallagownden. 2014. Optimization of neural network architecture using genetic algorithm for load forecasting. In: 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, Malaysia, p. 1–6
   Google Scholar
• Jamous, R., A. Tharwat, E. El. Seidy, and B. Bayoumi. 2015. A new particle swarm with center of mass optimization. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) 04 5 (May), 312-317.
   Google Scholar
• Jiansheng, W., O. JinL, and L. Mingzhe. January 19 2015. Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. In Neurocomputing Journal 148: 136–42. doi:https://doi.org/10.1016/j.neucom.2012.10.043.
   Web of Science ®Google Scholar
• Mustafa, A. S., and Y. S. K. Swamy. 2015. Web service classification using multi-layer perceptron optimized with Tabu search. In: Advance Computing Conference (IACC), 2015 IEEE International, Banglore, India, p. 290–94.
   Google Scholar
• Oludare, A., J. Aman, O. Abiodun, D. Kemi, M. Nachaat, and A. Humaira. 2018. State-of-the-art in artificial neural networkapplications: A survey. Heliyon.4 (11): e00938.
   Google Scholar
• Qolomany, B., M. Maabreh, A. Al-Fuqaha, A. Gupta, and D. Benhaddou 2017. Parameters optimization of deep learning models using particle swarm optimization, 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, 2017, Valencia, Spain, pp. 1285–90.
   Google Scholar
• Sengupta, S., S. Basak, and R. A. Peters. 2019. Particle swarm optimization: A survey of historical and recent developments with hybridization perspectives. Machine Learning and Knowledge Extraction 1 (1):157–91. doi:https://doi.org/10.3390/make1010010.
   Google Scholar
• Shi, Y., and R. Eberhart. 1999. Empirical study of particle swarm optimization. International Conference on Evolutionary. Washington, USA: Computation.
   Google Scholar
• Sohrab Z., M. A., L. Ali, E. Ali, and C. Ioannis. 2012. Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization. Geophysical Prospecting 61 (3):582-598.
   Web of Science ®Google Scholar
• Stang, M., C. Meier, V. Rau, and E. Sax. 2020. An evolutionary approach to hyper-parameter optimization of neural networks. In Human interaction and emerging technologies. IHIET 2019. Advances in intelligent systems and computing, vol 1018, ed. T. Ahram, R. Taiar, S. Colson, and A. Choplin. pp. 713-718. Cham: Springer.
   Google Scholar
• Xin, J., G. Chen, and Y. Hai. 2009. A particle swarm optimizer with multi-stage linearly-decreasing inertia weight. International Joint Conference on Computational Sciences and Optimization, Sanya, Hainan, pp. 505–08, doi: https://doi.org/10.1109/CSO.420.
   Google Scholar
• Xu, D., and Y. A. Tian. 2015. Comprehensive survey of clustering algorithms. Annals of Data Science 2 (2):165–93. doi:https://doi.org/10.1007/s40745-015-0040-1.
   Google Scholar
• Xu, Z., Zhang, J., Wang, J., and Xu Zh. 2020. Prediction research of financial time series based on deep learning. Soft Computing. 24(11):8295–312. doi:https://doi.org/10.1007/s00500-020-04788-w.
   Web of Science ®Google Scholar
• Yamasaki, T., T. Honma, and K. Aizawa. 2017. Efficient optimization of convolutional neural networks using particle swarm optimization. in: IEEE Third International Conference on Multimedia Big Data, Laguna Hills, CA, USA, pp. 70–73.
   Google Scholar
• Ye, F., and W.-B. Du. 2017. Particle swarm optimization based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data. PLoS ONE 12 (12):e0188746. doi:https://doi.org/10.1371/journal.pone.0188746.
   PubMed Web of Science ®Google Scholar
• Yulong, W., Z. Haoxin, and Z. Guangwei. 2019. cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks. Swarm and Evolutionary Computation 49:114–23. doi:https://doi.org/10.1016/j.swevo.2019.06.002.
   Web of Science ®Google Scholar
• Zajmi, L., H. A. Falah, and A. J. Adam. 2018. Concepts, methods, and performances of particle swarm optimization, backpropagation, and neural networks. Applied Computational Intelligence and Soft Computing vol:1–7. doi:https://doi.org/10.1155/2018/9547212.
   Google Scholar