Neural Network Training Using Hybrid Particlemove Artificial Bee Colony Algorithm for Pattern Classification

Authors

  • Zakaria Noor Aldeen Mahmood Al Nuaimi School of Computer Sciences Universiti Sains Malaysia, Malaysia
  • Rosni Abdullah School of Computer Sciences Universiti Sains Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jict2017.16.2.6

Keywords:

Swarm Intelligence, Artificial Neural Networks, Artificial Bee Colony Algorithm, Particle Swarm Optimization, Pattern-Classification

Abstract

The Artificial Neural Networks Training (ANNT) process is an optimization problem of the weight set which has inspired researchers for a long time. By optimizing the training of the neural networks using optimal weight set, better results can be obtained by the neural networks. Traditional neural networks algorithms such as Back Propagation (BP) were used for ANNT, but they have some drawbacks such as computational complexity and getting trapped in the local minima. Therefore, evolutionary algorithms like the Swarm Intelligence (SI) algorithms have been employed in ANNT to overcome such issues. Artificial Bees Colony (ABC) optimization algorithm is one of the competitive algorithms in the SI algorithms group. However, hybrid algorithms are also a fundamental concern in the optimization field, which aim to cumulate the advantages of different algorithms into one algorithm. In this work, we aimed to highlight the performance of the Hybrid Particle-move Artificial Bee Colony (HPABC) algorithm by applying it on the ANNT application. The performance of the HPABC algorithm was investigated on four benchmark pattern-classification datasets and the results were compared with other algorithms. The results obtained illustrate that HPABC algorithm can efficiently be used for ANNT. HPABC outperformed the original ABC and PSO as well as other state-of-art and hybrid algorithms in terms of time, function evaluation number and recognition accuracy.

 

References

Abusnaina, A. A., Abdullah, R., & Kattan, A. (2014). Enhanced MWO training algorithm to improve classification accuracy of artificial neural networks. Recent advances on soft computing and data mining (pp. 183-194): Springer.

Alqattan, Z. N., & Abdullah, R. (2015). A hybrid artificial bee colony algorithm for numerical function optimization. International Journal of Modern Physics C, 1550109. Journal of ICT, 16, No. 2 (Dec) 2017, pp: 314–

Bahamish, H. A. A., Abdullah, R., & Salam, R. A. (2009). Protein tertiary structure prediction using artificial bee colony algorithm. Paper presented at the Third Asia International Conference on Modelling & Simulation, AMS’09..

Blum, C., & Socha, K. (2005). Training feed-forward neural networks with ant colony optimization: An application to pattern classification. Paper presented at the Fifth International Conference on Hybrid Intelligent Systems, HIS’05.

Camargo, L. C., Correa Tissot, H., & Ramirez Pozo, A. T. (2012). Use of backpropagation and differential evolution algorithms to training MLPs. Paper presented at the 31st International Conference of the Chilean Computer Science Society (SCCC).

Dorigo, M., & Gambardella, L. M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. Evolutionary Computation, IEEE Transactions on, 1(1), 53-66.

Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

Farshidpour, S., & Keynia, F. (2012). Using artificial bee colony Algorithm for MLP Training on software defect prediction. Oriental Journal of Computer Science & Technology, 5(2).

Ghanem, W., & Jantan, A. (2014). Using hybrid artificial bee colony algorithm and particle swarm optimization for training feed-forward neural networks. Journal of Theoretical and Applied Information Technology, 67(3), 664-674.

Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization: Technical report-tr06 Erciyes university, engineering faculty, computer engineering department.

Karaboga, D., Akay, B., & Ozturk, C. (2007). Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. Modeling Decisions for Artificial Intelligence (pp. 318-329) Proceedings. Springer. Journal of ICT, 16, No. 2 (Dec) 2017, pp: 314–

Karaboga, D., & Basturk, B. (2007). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. Foundations of Fuzzy Logic and Soft Computing (pp. 789-798) Proceedings. Springer.

Karaboga, D., & Gorkemli, B. (2011). A combinatorial artificial bee colony algorithm for traveling salesman problem. Paper presented at the International Symposium on Innovations in Intelligent Systems and Applications (INISTA).

Karaboga, D., & Ozturk, C. (2010). Fuzzy clustering with artificial bee colony algorithm. Scientific Research and Essays, 5(14), 1899-1902.

Kattan, A., & Abdullah, R. (2011). Training of feed-forward neural networks for pattern-classification applications using music inspired algorithm. Training, 9(11).

Kattan, A., & Abdullah, R. (2013). Training feed-forward artificial neural networks for pattern-classification using the harmony search algorithm. Paper presented at the The Second International Conference on Digital Enterprise and Information Systems (DEIS2013).

Li, X.-l., Shao, Z.-j., & Qian, J.-x. (2002). An optimizing method based on autonomous animats: Fish-swarm algorithm. System Engineering Theory and Practice, 22(11), 32-38.

Lichman, K. B. a. M. (2013). UCI Machine Learning Repository. http://archive.ics.uci.edu/ml

Mahmood, Z. N., Mahmuddin, M., & Mahmood, M. N. (2012). Protein tertiary structure prediction based on main chain angle using a hybrid bees colony optimization algorithm. Paper presented at the International Journal of Modern Physics: Conference Series.

Montana, D. J., & Davis, L. (1989). Training feedforward neural networks using genetic algorithms. Paper presented at the IJCAI.

Mustaffa, Z., Yusof, Y., & Kamaruddin, S. (2013). Enhanced Abc-Lssvm for energy fuel price prediction. Journal of Information and Communication Technology, 12, 73-101.

Nur, A. S. (2015). Near optimal convergence of back-propagation method using harmony search. TELKOMNIKA Indonesian Journal of Electrical Engineering, 14(1). Journal of ICT, 16, No. 2 (Dec) 2017, pp: 314–

Ozturk, C., & Karaboga, D. (2011). Hybrid artificial bee colony algorithm for neural network training. Paper presented at the IEEE Congress on Evolutionary Computation (CEC).

Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. Control Systems, IEEE, 22(3), 52-67.

Satpathy, R. (2017). Bioinspired algorithms in solving three-dimensional protein structure prediction problems bio-inspired computing for information retrieval applications (pp. 316-337): IGI Global.

Sharma, T. K., Pant, M., & Bhardwaj, T. (2011). PSO ingrained artificial bee colony algorithm for solving continuous optimization problems. Paper presented at the IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE).

SyarifahAdilah, M., Abdullah, R., & Venkat, I. (2012). ABC algorithm as feature selection for biomarker discovery in mass spectrometry analysis. Paper presented at the 4th Conference on Data Mining and Optimization (DMO).

Vazquez, R. A., & Garro, B. A. (2015). Training spiking neural models using artificial bee colony. Computational Intelligence and Neuroscience, 2015, 18.

Yaghini, M., Khoshraftar, M. M., & Fallahi, M. (2013). A hybrid algorithm for artificial neural network training. Engineering Applications of Artificial Intelligence, 26(1), 293-301.

Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35-62.

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Published

06-11-2017

Issue

Vol. 16 No. 2 (2017): Journal of Information and Communication Technology (JICT) Vol.16, No.2, December 2017

Section

Articles

How to Cite

Mahmood Al Nuaimi, Z. N. A., & Abdullah, R. (2017). Neural Network Training Using Hybrid Particlemove Artificial Bee Colony Algorithm for Pattern Classification. Journal of Information and Communication Technology, 16(2), 314-334. https://doi.org/10.32890/jict2017.16.2.6

Research impact

Harvested 2026-09-22
10 citations, from OpenAlex — the highest of the sources checked

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Identifiers DOI 10.32890/jict2017.16.2.6 OpenAlex W4256075161

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