G-HABC algorithm for training artificial neural networks

Mat Deris, Mustafa and Shah, Habib and Ghazali, Rozaida and Mohd Nawi, Nazri (2012) G-HABC algorithm for training artificial neural networks. International Journal of Applied Metaheuristic Computing (IJAMC) .

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Official URL: http://dx.doi.org/10.4018/jamc.2012070101

Abstract

Learning problems for Neural Network (NN) has widely been explored in the past two decades. Researchers have focused more on population-based algorithms because of its natural behavior processing. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) algorithm produced an easy way for NN training. These social based techniques are mostly used for finding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has difficulties such as trapping in local minima and slow convergence. The new method named Global Hybrid Ant Bee Colony (G-HABC) algorithm which can overcome the gaps in BP is used to train the NN for Boolean Function classification task. The simulation results of the NN when trained with the proposed hybrid method were compared with that of Levenberg-Marquardt (LM) and ordinary ABC. From the results, the proposed G-HABC algorithm has shown to provide a better learning performance for NNs with reduced CPU time and higher success rates.

Item Type:Article
Uncontrolled Keywords:Ant Colony Optimization (ACO); Artificial Bee Colony (ABC); Global Hybrid Ant Bee Colony (G-HABC); Hybrid Ant Bee Colony Algorithm; swarm intelligence
Subjects:Q Science > QA Mathematics > QA75 Calculating machines > QA75.5 Electronic computers. Computer science
Divisions:Faculty of Science Computer and Information Technology > Department of Software Engineering
ID Code:3559
Deposited By:Normajihan Abd. Rahman
Deposited On:11 Apr 2013 08:54
Last Modified:11 Apr 2013 08:55

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