Training Of Artificial Neural Network Using Metaheuristic Algorithm
AbstractThis 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.
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-Verse Optimizer: a nature-inspired algorithm for global optimization,", Neural Comput & Applic Neural Computing and Applications, vol. 27, no. 2, pp. 495-513, 2016.
Copyright (c) 2017 International Journal of Intelligent Systems and Applications in Engineering
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.