Training Of Artificial Neural Network Using Metaheuristic Algorithm

This article clarify enhancing classification accuracy of Artificial Neural Network (ANN) by using metaheuristic optimization algorithm. Classification accuracy of ANN depends on the well-designed ANN model. Well-designed ANN model Based on the structure, activation function that are utilized for ANN nodes, and the training algorithm which are used to detect the correct weight for each node. In our paper we are focused on improving the set of synaptic weights by using Shuffled Frog Leaping metaheuristic optimization algorithm which are determine the correct weight for each node in ANN model. We used 10 well known datasets from UCI machine learning repository. In order to investigate the performance of ANN model we used datasets with different properties. These datasets have categorical, numerical and mixed properties. Then we compared the classification accuracy of proposed method with the classification accuracy of back propagation training algorithm. The results showed that the proposed algorithm performed better performance in the most used datasets.

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L. Wang and X. Fu, "Data mining with computational intelligence", 1th ed Berlin, Germany: Springer-Verlag, 2005.pp 276

M. Alhamdoosh and D. Wang, "Fast decorrelated neural network ensembles with random weights", INS Information Sciences, vol. 264, pp. 104-117, 2014.

S. S. Liew, M. Khalil-Hani, and R. Bakhteri, "An optimized second order stochastic learning algorithm for neural network training", Neurocomputing, vol. 186, no. 12, pp. 74-89, 2016.

P. A. Kowalski and S. Lukasik, "Training Neural Networks with Krill Herd Algorithm", Neural Process Lett, vol. 44, no. 1, pp. 5-17, 2016.

N. S. Jaddi, S. Abdullah, and A. R. Hamdan, "Optimization of neural network model using modified bat-inspired algorithm", ASOC Applied Soft Computing, vol. 37, pp. 71-86, 2015.

S. Chalup, F. Maire, and C. E. C. "A study on hill climbing algorithms for neural network training ", (in No Linguistic Content), vol. 3, Washington D.C., 2014–2021, 1999, vol. 3 .

R. S. Sexton, B. Alidaee, R. E. Dorsey, and J. D. Johnson, "Global optimization for artificial neural networks: A tabu search application", European Journal of Operational Research, vol. 106, no. 2, pp. 570-584, 1998.

K. G. Kapanova, I. Dimov, and J. M. Sellier, "A genetic approach to automatic neural network architecture optimization,", Neural Computing and Applications, no. 4, 2016.

D. Chakraborty, S. Saha, S. Maity, A., "Training feedforward neural networks using hybrid flower pollination-gravitational search algorithm", International Conference on Futuristic Trends on Computational, and M. Knowledge, pp. 261-266, 2015.

K. M. Salama and A. M. Abdelbar, "Learning neural network structures with ant colony algorithms", Swarm Intell Swarm Intelligence, vol. 9, no. 4, pp. 229-265, 2015.

S. Mirjalili, S. M. Mirjalili, and A. Hatamlou, "Multi-VerseOptimizer: a nature-inspired algorithm for global optimization,", Neural Comput & Applic Neural Computing and Applications, vol. 27, no. 2, pp. 495-513, 2016.

J. Kennedy, R. Eberhart, and I. I. C. o. N. N. "Particle swarm optimization ", Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948 vol.4, 1995.

V. G. Gudise, G. K. Venayagamoorthy, and I. S. I. S., "Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks," Proceedings of the IEEE, pp. 110-117, 2003.

H. Melo and J. Watada, "Gaussian-PSO with fuzzy reasoning based on structural learning for training a Neural Network", NEUCOM Neurocomputing, vol. 172, pp. 405-412, 2016.

S. Chatterjee, S. Sarkar, S. Hore, N. Dey, A. S. Ashour, and V. E. Balas, "Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings", Neural Comput & Applic Neural Computing and Applications, no. 9, 2016.

T. Liu and S. Yin, "An improved particle swarm optimization algorithm used for BP neural network and multimedia course- ware evaluation", Multimed Tools Appl Multimedia Tools and Applications, no. 5, 2016.

Sudip Mandal, G. Saha, and R. K. Pal, "Neural Network Training Using Firefly Algorithm," Global Journal on Advancement in Engineering and Science (GJAES), vol. 1, no. 1, 2015.

Hameed, A. A., Karlik, B. and Salman, M. S., "Back- propagation algorithm with variable adaptive momentum ", Knowledge-Based Systems, vol 114, pp 79-87,2016.

M. Eusuff, K. Lansey, and F. Pasha, "Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization", Engineering Optimization, vol. 38, no. 2, pp. 129-154, 2006.

J. Luo, X. Li, M.-R. Chen, and H. Liu, "A novel hybrid shuffled frog leaping algorithm for vehicle routing problem with time windows", Information Sciences Information Sciences, vol. 316, no. 1, pp. 266-292, 2015.

S. Panda, A. Sarangi, and S. P. Panigrahi, "A new training strategy for neural network using shuffled frog-leaping algorithm and application to channel equalization", AEU - International Journal of Electronics and Communications AEU - International Journal of Electronics and Communications, vol. 68, no. 11, pp. 1031-1036, 2014.

K. K. Bhattacharjee and S. P. Sarmah, "Shuffled frog leaping algorithm and its application to 0/1 knapsack problem", ASOC Applied Soft Computing Journal, vol. 19, pp. 252-263, 2014.

A. Abraham, J. L. Mauri, J. Buford, J. Suzuki, S. M. Thampi, " Advances in computing and communications " , Berlin, Germany: Springer-Verlag , 2011.