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Global hybrid ant bee colony algorithm for training artificial neural networks

Shah, Habib and Ghazali, Rozaida and Mohd Nawi, Nazri and Mat Deris, Mustafa (2012) Global hybrid ant bee colony algorithm for training artificial neural networks. In: ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications, 18-21 June 2012, Salvador de Bahia, Brazil.

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Learning problems for Neural Networks (NNs) has widely been explored from last two decades. Population based algorithms become more focus by researchers because of its nature behavior processing with optimal solution. The population-based algorithms are Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and recently Hybrid Ant Bee Colony (HABC) Algorithms produced easy way for training NNs. These social based techniques mostly used for finding optimal weight values and over trapping local minima in NNs learning. Typically, NNs trained by a traditional and recognized algorithm called Backpropagation (BP) has difficulties such as trapping in local minima, slow convergence or might fail sometimes. In this research, the new method named Global Hybrid Ant Bee Colony (GHABC) algorithm used to train NNs to recover the BP gaps. The simulation result of a hybrid algorithm evaluates with ordinary ABC, Levenberg-Marquardt (LM) training algorithms. From the investigated results, the proposed GHABC algorithm did get better the learning efficiency for NNs using Boolean Function classification task.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: swarm intelligence; ant colony optimization; artificial bee colony; global hybrid ant bee colony; hybrid ant bee colony algorithm
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 23 Apr 2015 00:30
Last Modified: 23 Apr 2015 00:30
URI: http://eprints.uthm.edu.my/id/eprint/3565
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