International Journal of Applied Metaheuristic Computing, 3(3), 1-19, July-September 2012 1 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Keywords: Ant Colony Optimization (ACO), Artifcial Bee Colony (ABC), Global Hybrid Ant Bee Colony (G-HABC), Hybrid Ant Bee Colony Algorithm, Swarm Intelligence 1. INTRODUCTION Nowadays, Neural Networks is widely used in different works such as: linear and nonlinear modeling, prediction and forecasting are mostly caused by their property of generality (Ghazali, Hussain, & Liatsis, 2011; Husaini et al., 2011; Ghazali et al., 2008; Osamu, 1998; Yan & Saif, 1993). It has powerful and flexible tools that were used successfully in various applications such as classification, statistical, biological, medical, industrial, mathematical, and software engineering (Curry & Rumelhart, 1990; Fionn, 1991; Thwin & Quah, 2005). Artificial Neural Networks learnt their training techniques by parallel processing. NNs tools are capable of achieving many scientific research applications by providing best network architecture, activa- tion function, input pre-processing and optimal weight values. NNs tools are the most interesting and understandable to mathematical problems and G-HABC Algorithm for Training Artifcial Neural Networks Habib Shah, Universiti Tun Hussein Onn Malaysia, Malaysia Rozaida Ghazali, Universiti Tun Hussein Onn Malaysia, Malaysia Nazri Mohd Nawi, Universiti Tun Hussein Onn Malaysia, Malaysia Mustafa Mat Deris, Universiti Tun Hussein Onn Malaysia, Malaysia 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), Artifcial 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 fnding best weight values and over trapping local minima in NN learning. Typically, NN trained by traditional approach, namely the Backpropagation (BP) algorithm, has diffculties 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 classifcation 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. DOI: 10.4018/jamc.2012070101