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Hybrid ant bee colony algorithm (HABC) for classifiation and prediction mission

Shah, Habib and Ghazali, Rozaida and Mohd Nawi, Nazri (2011) Hybrid ant bee colony algorithm (HABC) for classifiation and prediction mission. In: 2nd World Conference on Information Technology (WCIT-2011), 23-26 November 2011, Antalya, Turkey.

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Abstract

A combine social insect's movement attracts scientists toward new solutions for different mathematical and statistical problems. Chief among of them are the Artificial Bee Colony (ABC) algorithm and Ant Colony Optimization (ACO) algorithm that simulate the intelligent foraging behaviours of honey bee and ant swarms. These algorithms have been successfully used in many different tasks such as classification, global optimization for numerical function, image segmentation and optimization of Artificial Neural Algorithm (ANNs) weights. Multilayer perception (MLP), the widely known ANNs was normally trained with the standard Back-Propagation (BP) algorithm for minimizing the network error. However, using the BP algorithm for training the MLP always contribute to a problem of suboptimal weights because of the proposed, and it is called Hybrid Bee Ant Colony (BBAC) algorithm. In this work, HBAC is used for training the MLP and XOR classification tasks. The performance of the proposed HBAC algorithm is benchmarked against the standard BP. Experimental results show that the proposed HBAC algorithm outperformed the BP and ACO algorithms when used to train the MLP with lower prediction error.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: artificial bee colony algorithm; ant colony optimization and hybrid bee ant 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: 07 Feb 2013 05:37
Last Modified: 21 Jan 2015 07:19
URI: http://eprints.uthm.edu.my/id/eprint/2963
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