An improved bat algorithm with artificial neural networks for classification problems

Metaheuristic search algorithms have been used for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Moreover several algorithms b...

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Main Author:	Rehman Gillani, Syed Muhammad Zubair
Format:	Thesis
Language:	English English English
Published:	2016
Subjects:	QA Mathematics QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/10043/1/24p%20SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI.pdf http://eprints.uthm.edu.my/10043/2/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10043/3/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%20WATERMARK.pdf
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spelling	my-uthm-ep.100432023-10-01T07:04:40Z An improved bat algorithm with artificial neural networks for classification problems 2016-05 Rehman Gillani, Syed Muhammad Zubair QA Mathematics QA76 Computer software Metaheuristic search algorithms have been used for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Moreover several algorithms belonging to the stochastic and detenninistic classes are available (i.e. ABC, HS, CS, WS, BPNN, LM, and ERNN etc.). Recently, a new metaheuristic search Bat algorithm has become quite popular due its tendency towards convergence to optimal points in the search trajectory by using echo-location behavior of bats as its random walk. However, Bat suffers from large step lengths that sometimes make it to converge to sub-optimal solution. Therefore, in order to improve the exploration and exploitation behavior of bats, this research proposed an improved Bat with Gaussian Distribution (BAGD) algorithm that takes small step lengths and ensures convergence to global optima. Then, the proposed BAGD algorithm is frnther hybridized with Simulated Annealing (SA) and Genetic Algorithm (GA) to perform two stage optimization in which the former algorithm finds the optimal solution and the latter algorithm starts from where the first one is converged. This multi-stage optimization ensures that optimal solution is always reached. The proposed BAGD, SABa, and GBa are tested on several benchmark functions and improvements in convergence to global optima were detected. Finally in this research, the proposed BAGD, SABa, an.::l GBa are used to enhance the convergence properties ofBPNN, LM, and ERNN with proper estimation of the initial weights. The proposed Bat variants with Al',IN such as; Bat-BP, BALM, BAGD-LM, BAGD-RNN, GBa-LM, GBa-RNN, 01\ H· -RN J, an 1 ,., ABa-LM are evaluated and compared with ABC-BP, and ABC-] i•vf : l 2016-05 Thesis http://eprints.uthm.edu.my/10043/ http://eprints.uthm.edu.my/10043/1/24p%20SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI.pdf text en public http://eprints.uthm.edu.my/10043/2/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/10043/3/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%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 English
topic	QA Mathematics QA76 Computer software
spellingShingle	QA Mathematics QA76 Computer software Rehman Gillani, Syed Muhammad Zubair An improved bat algorithm with artificial neural networks for classification problems
description	Metaheuristic search algorithms have been used for quite a while to optimally solve complex searching problems with ease. Nowadays, nature inspired swarm intelligent algorithms have become quite popular due to their propensity for finding optimal solutions with agility. Moreover several algorithms belonging to the stochastic and detenninistic classes are available (i.e. ABC, HS, CS, WS, BPNN, LM, and ERNN etc.). Recently, a new metaheuristic search Bat algorithm has become quite popular due its tendency towards convergence to optimal points in the search trajectory by using echo-location behavior of bats as its random walk. However, Bat suffers from large step lengths that sometimes make it to converge to sub-optimal solution. Therefore, in order to improve the exploration and exploitation behavior of bats, this research proposed an improved Bat with Gaussian Distribution (BAGD) algorithm that takes small step lengths and ensures convergence to global optima. Then, the proposed BAGD algorithm is frnther hybridized with Simulated Annealing (SA) and Genetic Algorithm (GA) to perform two stage optimization in which the former algorithm finds the optimal solution and the latter algorithm starts from where the first one is converged. This multi-stage optimization ensures that optimal solution is always reached. The proposed BAGD, SABa, and GBa are tested on several benchmark functions and improvements in convergence to global optima were detected. Finally in this research, the proposed BAGD, SABa, an.::l GBa are used to enhance the convergence properties ofBPNN, LM, and ERNN with proper estimation of the initial weights. The proposed Bat variants with Al',IN such as; Bat-BP, BALM, BAGD-LM, BAGD-RNN, GBa-LM, GBa-RNN, 01\ H· -RN J, an 1 ,., ABa-LM are evaluated and compared with ABC-BP, and ABC-] i•vf : l
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Rehman Gillani, Syed Muhammad Zubair
author_facet	Rehman Gillani, Syed Muhammad Zubair
author_sort	Rehman Gillani, Syed Muhammad Zubair
title	An improved bat algorithm with artificial neural networks for classification problems
title_short	An improved bat algorithm with artificial neural networks for classification problems
title_full	An improved bat algorithm with artificial neural networks for classification problems
title_fullStr	An improved bat algorithm with artificial neural networks for classification problems
title_full_unstemmed	An improved bat algorithm with artificial neural networks for classification problems
title_sort	improved bat algorithm with artificial neural networks for classification problems
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2016
url	http://eprints.uthm.edu.my/10043/1/24p%20SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI.pdf http://eprints.uthm.edu.my/10043/2/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/10043/3/SYED%20MUHAMMAD%20ZUBAIR%20REHMAN%20GILLANI%20WATERMARK.pdf
_version_	1783728984006066176

An improved bat algorithm with artificial neural networks for classification problems

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