Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) Algorithm for Classification

Habib Shah, Rozaida Ghazali, Nazri Mohd Nawi, Mustafa Mat Deris and Tutut Herawan
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Habib Shah: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia
Rozaida Ghazali: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia
Nazri Mohd Nawi: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia
Mustafa Mat Deris: Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Johor, Malaysia
Tutut Herawan: Department of Mathematics Education, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

International Journal of Applied Evolutionary Computation (IJAEC), 2013, vol. 4, issue 3, 58-74

Abstract: The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and Global Artificial Bee Colony Algorithm train Multilayer Perceptron (MLP). This paper investigates the new hybrid technique called Global Artificial Bee Colony-Levenberq-Marquardt (GABC-LM) algorithm. One of the crucial problems with the BP algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome GABC-LM algorithm used in this work to train MLP for the boolean function classification task, the performance of GABC-LM is benchmarked against MLP training with the typical LM, PSO, ABC and GABC. The experimental result shows that GABC-LM performs better than that standard BP, ABC, PSO and GABC for the classification task.

Date: 2013
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