Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and it...

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Main Author:	Abro, Abdul Ghani
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK1-9971 Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.usm.my/45016/1/Abdul%20Ghani%20Abro24.pdf
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id	my-usm-ep.45016
record_format	uketd_dc
spelling	my-usm-ep.450162019-07-23T02:59:16Z Performance Enhancement Of Artificial Bee Colony Optimization Algorithm 2013-07 Abro, Abdul Ghani TK1-9971 Electrical engineering. Electronics. Nuclear engineering Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and its variants have been found to suffer from slow convergence, prone to local-optima traps, poor exploitation and poor capability to replace exhaustive potential-solutions. To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum. All the proposed ABC variants have been incorporated with a proposed intelligent scout-bee scheme whilst MBABC and EABC employ a novel elite-update scheme. 2013-07 Thesis http://eprints.usm.my/45016/ http://eprints.usm.my/45016/1/Abdul%20Ghani%20Abro24.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	TK1-9971 Electrical engineering Electronics Nuclear engineering
spellingShingle	TK1-9971 Electrical engineering Electronics Nuclear engineering Abro, Abdul Ghani Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
description	Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and its variants have been found to suffer from slow convergence, prone to local-optima traps, poor exploitation and poor capability to replace exhaustive potential-solutions. To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum. All the proposed ABC variants have been incorporated with a proposed intelligent scout-bee scheme whilst MBABC and EABC employ a novel elite-update scheme.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Abro, Abdul Ghani
author_facet	Abro, Abdul Ghani
author_sort	Abro, Abdul Ghani
title	Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
title_short	Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
title_full	Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
title_fullStr	Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
title_full_unstemmed	Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
title_sort	performance enhancement of artificial bee colony optimization algorithm
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2013
url	http://eprints.usm.my/45016/1/Abdul%20Ghani%20Abro24.pdf
_version_	1747821438736269312

Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

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