G-HABC Algorithm for Training Artificial Neural Networks

Author:

Shah Habib1,Ghazali Rozaida2,Nawi Nazri Mohd2,Deris Mustafa Mat1

Affiliation:

1. Universiti Tun Hussein Onn Malaysia, Malaysia

2. Universiti Tun Hussein Onn, 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), 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.

Publisher

IGI Global

Subject

Decision Sciences (miscellaneous),Computational Mathematics,Computational Theory and Mathematics,Control and Optimization,Computer Science Applications,Modeling and Simulation,Statistics and Probability

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