Review Optimized Artificial Neural Network by Meta-Heuristic Algorithm and its Applications
DOI:
https://doi.org/10.29304/jqcm.2021.13.3.825Keywords:
Artificial neural network, metaheuristic optimizing algorithms, prediction; classificationAbstract
A Meta-Heuristic Algorithms Optimization (MAHO) is inspired by nature. The Artificial neural network (ANN) has been shown to be successful in a variety of applications, including machine learning. ANNs were optimized using meta-optimization methods to enhance classification performance and predictions. The fundamental objective of combining a meta-heuristic algorithm (MHAO) with an artificial neural network (ANN) is to train the network to update the weights. The training would be speedier than with a standard ANN since it will use a meta-heuristic method with global optimal searching capability to avoid local minimum and will also optimize difficult problems. will discuss some of these meta-heuristic algorithms using ANN as they are applied to common data sets, as well as real-time specific classification and prediction experiences. In order to give researchers motivational insights into their own fields of application.
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References
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[7] N. T. Dat, C. V. Kien, H. P. H. Anh and N. N. Son, "Parallel Multi-Population Technique for Meta-Heuristic Algorithms on Multi Core Processor," 2020 5TH INTERNATIONAL CONFERENCE ON GREEN TECHNOLOGY AND SUSTAINABLE DEVELOPMENT (GTSD), 2020, pp. 489-494, doi: 10.1109/GTSD50082.2020.9303114.
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meta heuristic model for optimizing weights of artificial neural network” 2020, doi:
10.11591/ijeecs. v20.i2. pp976-984.
optimizing weights of artificial neural network,” Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 2, pp. 976-984,
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[15] R. K. Sivagaminathan and S. Ramakrishnan, “A hybrid approach for feature subset selection using neural networks and ant colony optimization,” Expert Syst. Appl., vol. 33, no. 1, pp. 49–60, 2007.
[16] H. Shi and W. Li, “Artificial neural networks with ant colony optimization for assessing performance of residential buildings,” in 2009 International Conference on Future BioMedical Information Engineering (FBIE), 2009, pp. 379–382.
[17] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” J. Glob. Optim., vol. 39, no. 3, pp. 459–471, 2007.
[18] Taher, A., & Kadhimb, S. (2019). Hybrid between Genetic Algorithm and Artificial Bee Colony for Key
Generation Purpose. Journal of Al-Qadisiyah for Computer Science and Mathematics, 11(4), Comp Page 37-
46. https://doi.org/10.29304/jqcm.2019.11.4.627
[19] D. Karaboga and C. Ozturk, “Neural networks training by artificial bee colony algorithm on pattern classification,” Neural Netw. World, vol. 19, no. 3, p. 279, 2009.
[20] B. A. Garro, H. Sossa, and R. A. Vázquez, “Artificial neural network synthesis by means of artificial bee colony (abc) algorithm,” in 2011 IEEE Congress of Evolutionary Computation (CEC), 2011, pp. 331–338.
[21] S. Nandy, P. P. Sarkar, and A. Das, “Training a feed-forward neural network with artificial bee colony based backpropagation method,” arXiv Prepr. arXiv1209.2548, 2012.
[22] W.-C. Yeh and T.-J. Hsieh, “Artificial bee colony algorithm-neural networks for S-system models of biochemical networks approximation,” Neural Comput. Appl., vol. 21, no. 2, pp. 365–375, 2012.
[23] I. A. A. Al-Hadi, S. Z. M. Hashim, and S. M. H. Shamsuddin, “Bacterial Foraging Optimization Algorithm for neural network learning enhancement,” in 2011 11th international conference on hybrid intelligent systems (HIS), 2011, pp. 200–205.
[24] N. M. Nawi, M. Z. Rehman, and A. Khan, “A new bat based back-propagation (BAT-BP) algorithm,” in Advances in Systems Science, Springer, 2014, pp. 395–404.
[25] N. S. Jaddi, S. Abdullah, and A. R. Hamdan, “Multi-population cooperative bat algorithm-based optimization of artificial neural network model,” Inf. Sci. (Ny)., vol. 294, pp. 628–644, 2015.
[26] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Let a biogeography-based optimizer train your multi-layer perceptron,” Inf. Sci. (Ny)., vol. 269, pp. 188–209, 2014.
[27] Y. Zhang, P. Phillips, S. Wang, G. Ji, J. Yang, and J. Wu, “Fruit classification by biogeography-based optimization and feedforward neural network,” Expert Syst., vol. 33, no. 3, pp. 239–253, 2016.
[28] A. Rodan, H. Faris, J. Alqatawna, and others, “Optimizing feedforward neural networks using biogeography based optimization for e-mail spam identification,” Int. J. Commun. Netw. Syst. Sci., vol. 9, no. 01, p. 19, 2016.
[29] A. Askarzadeh and A. Rezazadeh, “A new heuristic optimization algorithm for modeling of proton exchange membrane fuel cell: bird mating optimizer,” Int. J. Energy Res., vol. 37, no. 10, pp. 1196–1204, 2013.
[30] A. Askarzadeh and A. Rezazadeh, “Artificial neural network training using a new efficient optimization algorithm,” Appl. Soft Comput., vol. 13, no. 2, pp. 1206–1213, 2013.
[31] X.-S. Yang, “Firefly algorithms for multimodal optimization,” in International symposium on stochastic algorithms, 2009, pp. 169–178.
[32] S. Nandy, P. P. Sarkar, and A. Das, “Analysis of a nature inspired firefly algorithm based back-propagation neural network training,” arXiv Prepr. arXiv1206.5360, 2012.
[33] I. Brajevic and M. Tuba, “Training feed-forward neural networks using firefly algorithm,” Recent Adv. Knowl. Eng. Syst. Sci., 2013.
[34] M. Alweshah, “Firefly algorithm with artificial neural network for time series problems,” Res. J. Appl. Sci. Eng. Technol., vol. 7, no. 19, pp. 3978–3982, 2014.
[35] D. E. Golberg, “Genetic algorithms in search, optimization, and machine learning,” Addion wesley, vol. 1989, no. 102, p. 36, 1989.
[36] A. J. F. van Rooij, R. P. Johnson, and L. C. Jain, Neural network training using genetic algorithms. World Scientific Publishing Co., Inc., 1996.
[37] U. Seiffert, “Multiple layer perceptron training using genetic algorithms,” in ESANN, 2001, pp. 159–164.
[38] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014.
[39] S. Mirjalili, “How effective is the Grey Wolf optimizer in training multi-layer perceptrons,” Appl. Intell., vol. 43, no. 1, pp. 150–161, 2015.
[40] A. H. Gandomi and A. H. Alavi, “Krill herd: a new bio-inspired optimization algorithm,” Commun. nonlinear Sci. Numer. Simul., vol. 17, no. 12, pp. 4831–4845, 2012.
[41] N. S. Lari and M. S. Abadeh, “Training artificial neural network by krill-herd algorithm,” in 2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference, 2014, pp. 63–67.
[42] P. A. Kowalski and S. Łukasik, “Training neural networks with krill herd algorithm,” Neural Process. Lett., vol. 44, no. 1, pp. 5–17, 2016.
[43] S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-based Syst., vol. 89, pp. 228–249, 2015.
[44] W. Yamany, M. Fawzy, A. Tharwat, and A. E. Hassanien, “Moth-flame optimization for training multi-layer perceptrons,” in 2015 11th International computer engineering Conference (ICENCO), 2015, pp. 267–272.
[45] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95-international conference on neural networks, 1995, vol. 4, pp. 1942–1948.
[46] M. Yaghini, M. M. Khoshraftar, and M. Fallahi, “A hybrid algorithm for artificial neural network training,” Eng. Appl. Artif. Intell., vol. 26, no. 1, pp. 293–301, 2013.
[47] Z. Beheshti and S. M. H. Shamsuddin, “CAPSO: centripetal accelerated particle swarm optimization,” Inf. Sci. (Ny)., vol. 258, pp. 54–79, 2014.
[48] R. Tang, S. Fong, X.-S. Yang, and S. Deb, “Wolf search algorithm with ephemeral memory,” in Seventh international conference on digital information management (ICDIM 2012), 2012, pp. 165–172.
[49] N. M. Nawi, M. Z. Rehman, and A. Khan, “WS-BP: An efficient wolf search based back-propagation algorithm,” in AIP Conference Proceedings, 2015, vol. 1660, no. 1, p. 50027.
[50] S. Mirjalili, “Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,” Neural Comput. Appl., vol. 27, no. 4, pp. 1053–1073, 2016.
[51] G.-G. Wang, S. Deb, and Z. Cui, “Monarch butterfly optimization,” Neural Comput. Appl., vol. 31, no. 7, pp. 1995–2014, 2019.
[52] D. Devikanniga and R. J. S. Raj, “Classification of osteoporosis by artificial neural network based on monarch butterfly optimisation algorithm,” Healthc. Technol. Lett., vol. 5, no. 2, pp. 70–75, 2018
[53] N. S. Jaddi, S. Abdullah, and A. R. Hamdan, “Optimization of neural network model using modified bat-inspired algorithm,” Appl. Soft Comput., vol. 37, pp. 71–86, 2015.
[54] L. L. Kamal and H. Kodaz, “Training artificial neural network by bat optimization algorithms,” Int. J. Adv. Comput. Eng. Netw., vol. 5, no. 8, pp. 53–56, 2017.
[55] M. Mavrovouniotis and S. Yang, “Training neural networks with ant colony optimization algorithms for pattern classification,” Soft Comput., vol. 19, no. 6, pp. 1511–1522, 2015.
[56] A. Asuncion and D. Newman, “UCI machine learning repository.” Irvine, CA, USA, 2007.
[57] A. H. Gandomi, X.-S. Yang, A. H. Alavi, and S. Talatahari, “Bat algorithm for constrained optimization tasks,” Neural Comput. Appl., vol. 22, no. 6, pp. 1239–1255, 2013.
[58] V. N. Ghate and S. V Dudul, “Optimal MLP neural network classifier for fault detection of three phase induction motor,” Expert Syst. Appl., vol. 37, no. 4, pp. 3468–3481, 2010.
[59] M. Mavrovouniotis and S. Yang, “Evolving neural networks using ant colony optimization with pheromone trail limits,” in 2013 13th UK Workshop on Computational Intelligence (UKCI), 2013, pp. 16–23.
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Published
2021-08-07
How to Cite
Kadhim, N. H., & Mosa, D. Q. (2021). Review Optimized Artificial Neural Network by Meta-Heuristic Algorithm and its Applications. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(3), Comp Page 34 – 46. https://doi.org/10.29304/jqcm.2021.13.3.825
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