Open Access
Issue
E3S Web of Conf.
Volume 469, 2023
The International Conference on Energy and Green Computing (ICEGC’2023)
Article Number 00019
Number of page(s) 13
DOI https://doi.org/10.1051/e3sconf/202346900019
Published online 20 December 2023
  1. R. King, “Artificial neural networks and computational intelligence,” IEEE Computer Applications in Power, vol. 11, no. 4, pp. 14–16, 1998. [CrossRef] [Google Scholar]
  2. S. Halpin and R. Burch, “Applicability of neural networks to industrial and commercial power systems: a tutorial overview,” IEEE Transactions on Industry Applications, vol. 33, no. 5, pp. 1355–1361, 1997. [CrossRef] [Google Scholar]
  3. A. Jain, J. Mao, and K. Mohiuddin, “Artificial neural networks: a tutorial,” Computer, vol. 29, no. 3, pp. 31–44, 1996. [CrossRef] [Google Scholar]
  4. Q.-J. Zhang, K. Gupta, and V. Devabhaktuni, “Artificial neural networks for rf and microwave design - from theory to practice,” IEEE Transactions on Microwave Theory and Techniques, vol. 51, no. 4, pp. 1339–1350, 2003. [CrossRef] [Google Scholar]
  5. M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial neural networks-based machine learning for wireless networks: A tutorial,” IEEE Communications Surveys Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019. [CrossRef] [Google Scholar]
  6. L. C. Jain and A. M. Fanelli, Recent advances in artificial neural networks : design and applications / edited by Lakhmi Jain, Anna Maria Fanelli. CRC Press Boca Raton, 2000. [Google Scholar]
  7. A. Zayegh and N. A. Bassam, “Neural network principles and applications,” in Digital Systems, V. Asadpour, Ed. Rijeka: IntechOpen, 2018, ch. 7. [Online]. Available: https://doi.org/10.5772/intechopen.80416 [Google Scholar]
  8. K. Mehrotra, C. Mohan, and S. Ranka, Elements of Artificial Neural Networks, ser. A Bradford book. MIT Press, 1997. [Online]. Available: https://books.google.co.in/books?id=6d68Y4Wq R4C [Google Scholar]
  9. M. Zhang and M. Zhang, Artificial Higher Order Neural Networks for Economics and Business. USA: IGI Global, 2008. [Google Scholar]
  10. S. Rukhaiyar, M. N. Alam, and N. K. Samadhiya, “A pso-ann hybrid model for predicting factor of safety of slope,” International Journal of Geotechnical Engineering, vol. 12, no. 6, pp. 556–566, 2018. [Online]. Available: https://doi.org/10.1080/19386362.2017.1305652 [Google Scholar]
  11. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948 vol.4. [CrossRef] [Google Scholar]
  12. R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39–43. [Google Scholar]
  13. R. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), vol. 1, 2000, pp. 84–88 vol.1. [CrossRef] [Google Scholar]
  14. M. Clerc and J. Kennedy, “The particle swarm - explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. [CrossRef] [Google Scholar]
  15. Y. del Valle, G. K. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, and R. G. Harley, “Particle swarm optimization: Basic concepts, variants and applications in power systems,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 2, pp. 171–195, 2008. [Google Scholar]
  16. M. N. Alam, B. Das, and V. Pant, “A comparative study of metaheuristic optimization approaches for directional overcurrent relays coordination,” Electric Power Systems Research, vol. 128, pp. 39–52, 2015. [CrossRef] [Google Scholar]
  17. MATLAB, Mathworks Inc., Massachusetts, USA, version R2018a. [Google Scholar]
  18. J. Liang, A. Qin, P. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 3, pp. 281–295, 2006. [Google Scholar]
  19. C. Coello, G. Pulido, and M. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256–279, 2004. [CrossRef] [Google Scholar]
  20. J.-H. Seo, C.-H. Im, C.-G. Heo, J.-K. Kim, H.-K. Jung, and C.-G. Lee, “Multimodal function optimization based on particle swarm optimization,” IEEE Transactions on Magnetics, vol. 42, no. 4, pp. 1095–1098, 2006. [CrossRef] [Google Scholar]
  21. Z.-L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Transactions on Power Systems, vol. 18, no. 3, pp. 1187–1195, 2003. [Google Scholar]
  22. J. Lu, D. Ireland, and A. Lewis, “Multi-objective optimization in high frequency electromagnetics—an effective technique for smart mobile terminal antenna (smta) design,” IEEE Transactions on Magnetics, vol. 45, no. 3, pp. 1072–1075, 2009. [CrossRef] [Google Scholar]
  23. Z.-L. Gaing, “A particle swarm optimization approach for optimum design of pid controller in avr system,” IEEE Transactions on Energy Conversion, vol. 19, no. 2, pp. 384–391, 2004. [CrossRef] [Google Scholar]
  24. R. Abousleiman and O. Rawashdeh, “Electric vehicle modelling and energy-efficient routing using particle swarm optimisation,” IET Intelligent Transport Systems, vol. 10, pp. 65–72(7), March 2016. [Online]. Available: https://digital-library.theiet.org/content/journals/10.1049/iet- its.2014.0177 [CrossRef] [Google Scholar]
  25. M. N. Alam, A. Mathur, and K. Kumar, “Economic load dispatch using a differential particle swarm optimization,” in 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2016, pp. 1–5. [Google Scholar]
  26. M. N. Alam, K. Kumar, and A. Mathur, “Economic load dispatch considering valve-point effects using time varying constriction factor based particle swarm optimization,” in 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON), 2015, pp. 1–6. [Google Scholar]
  27. M. Hedayati, Z. H. Firouzeh, and H. K. Nekoei, “Hybrid quantum particle swarm optimisation to calculate wideband green's functions for microstrip structures,” IET Microwaves, Antennas & Propagation, vol. 10, pp. 264–270(6), February 2016. [Online]. Available: https://digitallibrary.theiet.org/content/journals/10.1049/iet-map.2015.0169 [Google Scholar]
  28. X. Xia, H. Song, Y. Zhang, L. Gui, X. Xu, K. Li, and Y. Li, “A particle swarm optimization with adaptive learning weights tuned by a multiple-input multiple-output fuzzy logic controller,” IEEE Transactions on Fuzzy Systems, pp. 1–15, 2022. [Google Scholar]
  29. L. Ge, Y. Li, J. Yan, Y. Wang, and N. Zhang, “Short-term load prediction of integrated energy system with wavelet neural network model based on improved particle swarm optimization and chaos optimization algorithm,” Journal of Modern Power Systems and Clean Energy, pp. 1– 12, 2021. [Google Scholar]
  30. L. Hu, Y. Yang, Z. Tang, Y. He, and X. Luo, “Fcan-mopso: An improved fuzzy-based graph clustering algorithm for complex networks with multi-objective particle swarm optimization,” IEEE Transactions on Fuzzy Systems, pp. 1–16, 2023. [Google Scholar]

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