An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

Learning an Artificial Neural Network (ANN) is an optimization task since it is desirable to find optimal weight sets of an ANN in the training process. Different equations are used to guide the network for providing an accurate result with less training and testing error. Most of the training al...

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Main Author:	Shah, Habib
Format:	Thesis
Language:	English English
Published:	2014
Subjects:	QA76 Computer software QA71-90 Instruments and machines
Online Access:	http://eprints.uthm.edu.my/1214/1/24p%20HABIB%20SHAH.pdf http://eprints.uthm.edu.my/1214/2/HABIB%20SHAH%20WATERMARK.pdf
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spelling	my-uthm-ep.12142021-09-30T06:28:44Z An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction 2014-03 Shah, Habib QA76 Computer software QA71-90 Instruments and machines Learning an Artificial Neural Network (ANN) is an optimization task since it is desirable to find optimal weight sets of an ANN in the training process. Different equations are used to guide the network for providing an accurate result with less training and testing error. Most of the training algorithms focus on weight values, activation functions, and network structures for providing optimal outputs. Backpropagation (BP) learning algorithm is the well-known learning technique that trained ANN. However, some difficulties arise where the BP cannot get achievements without trapping in local minima and converge very slow in the solution space. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony (ABC) algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron (MLP). Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. They are: Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial Bee Colony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. These improved algorithms were used to increase the exploration, exploitation and keep them balance for getting optimal results for a given task. Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. The simulation results of the MLP trained with improved algorithms were compared with that when trained with the standard BP, ABC, Global ABC and Particle Swarm Optimization algorithm. From the experimental analysis, the proposed improved algorithms get better the classification efficacy for time series prediction and Boolean function classification. Moreover, these improved algorithm's success to get high accuracy and optimize the best network's weight values for training the MLP. 2014-03 Thesis http://eprints.uthm.edu.my/1214/ http://eprints.uthm.edu.my/1214/1/24p%20HABIB%20SHAH.pdf text en public http://eprints.uthm.edu.my/1214/2/HABIB%20SHAH%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English
topic	QA76 Computer software QA71-90 Instruments and machines
spellingShingle	QA76 Computer software QA71-90 Instruments and machines Shah, Habib An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
description	Learning an Artificial Neural Network (ANN) is an optimization task since it is desirable to find optimal weight sets of an ANN in the training process. Different equations are used to guide the network for providing an accurate result with less training and testing error. Most of the training algorithms focus on weight values, activation functions, and network structures for providing optimal outputs. Backpropagation (BP) learning algorithm is the well-known learning technique that trained ANN. However, some difficulties arise where the BP cannot get achievements without trapping in local minima and converge very slow in the solution space. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony (ABC) algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron (MLP). Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. They are: Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial Bee Colony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. These improved algorithms were used to increase the exploration, exploitation and keep them balance for getting optimal results for a given task. Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. The simulation results of the MLP trained with improved algorithms were compared with that when trained with the standard BP, ABC, Global ABC and Particle Swarm Optimization algorithm. From the experimental analysis, the proposed improved algorithms get better the classification efficacy for time series prediction and Boolean function classification. Moreover, these improved algorithm's success to get high accuracy and optimize the best network's weight values for training the MLP.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Shah, Habib
author_facet	Shah, Habib
author_sort	Shah, Habib
title	An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
title_short	An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
title_full	An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
title_fullStr	An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
title_full_unstemmed	An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
title_sort	improved artificial bee colony algorithm for training multilayer perceptron in time series prediction
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2014
url	http://eprints.uthm.edu.my/1214/1/24p%20HABIB%20SHAH.pdf http://eprints.uthm.edu.my/1214/2/HABIB%20SHAH%20WATERMARK.pdf
_version_	1747830754020163584

An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

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