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Journal of Nanomaterials
Volume 2017, Article ID 9702384, 6 pages
Research Article

An Accurate PSO-GA Based Neural Network to Model Growth of Carbon Nanotubes

1Department of Engineering, Macquarie University, Sydney, NSW 2109, Australia
2School of Engineering, Deakin University, Waurn Ponds, VIC 3125, Australia
3School of Biomedical Engineering, University of Technology, Sydney, NSW 2007, Australia

Correspondence should be addressed to Mohsen Asadnia; ua.ude.qm@aindasa.neshom

Received 12 April 2017; Revised 10 July 2017; Accepted 25 July 2017; Published 6 September 2017

Academic Editor: Yasuhiko Hayashi

Copyright © 2017 Mohsen Asadnia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


By combining particle swarm optimization (PSO) and genetic algorithms (GA) this paper offers an innovative algorithm to train artificial neural networks (ANNs) for the purpose of calculating the experimental growth parameters of CNTs. The paper explores experimentally obtaining data to train ANNs, as a method to reduce simulation time while ensuring the precision of formal physics models. The results are compared with conventional particle swarm optimization based neural network (CPSONN) and Levenberg–Marquardt (LM) techniques. The results show that PSOGANN can be successfully utilized for modeling the experimental parameters that are critical for the growth of CNTs.