An improved bees algorithm local search mechanism for numerical dataset

Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA i...

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Main Author:	Al-Dawoodi, Aras Ghazi Mohammed
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	https://etd.uum.edu.my/5622/1/s813731_01.pdf https://etd.uum.edu.my/5622/2/s813731_02.pdf
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id	my-uum-etd.5622
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mahmuddin, Massudi
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Al-Dawoodi, Aras Ghazi Mohammed An improved bees algorithm local search mechanism for numerical dataset
description	Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Al-Dawoodi, Aras Ghazi Mohammed
author_facet	Al-Dawoodi, Aras Ghazi Mohammed
author_sort	Al-Dawoodi, Aras Ghazi Mohammed
title	An improved bees algorithm local search mechanism for numerical dataset
title_short	An improved bees algorithm local search mechanism for numerical dataset
title_full	An improved bees algorithm local search mechanism for numerical dataset
title_fullStr	An improved bees algorithm local search mechanism for numerical dataset
title_full_unstemmed	An improved bees algorithm local search mechanism for numerical dataset
title_sort	improved bees algorithm local search mechanism for numerical dataset
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5622/1/s813731_01.pdf https://etd.uum.edu.my/5622/2/s813731_02.pdf
_version_	1747827958976872448
spelling	my-uum-etd.56222021-03-18T03:08:52Z An improved bees algorithm local search mechanism for numerical dataset 2015 Al-Dawoodi, Aras Ghazi Mohammed Mahmuddin, Massudi Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA75 Electronic computers. Computer science Bees Algorithm (BA), a heuristic optimization procedure, represents one of the fundamental search techniques is based on the food foraging activities of bees. This algorithm performs a kind of exploitative neighbourhoods search combined with random explorative search. However, the main issue of BA is that it requires long computational time as well as numerous computational processes to obtain a good solution, especially in more complicated issues. This approach does not guarantee any optimum solutions for the problem mainly because of lack of accuracy. To solve this issue, the local search in the BA is investigated by Simple swap, 2-Opt and 3-Opt were proposed as Massudi methods for Bees Algorithm Feature Selection (BAFS). In this study, the proposed extension methods is 4-Opt as search neighbourhood is presented. This proposal was implemented and comprehensively compares and analyse their performances with respect to accuracy and time. Furthermore, in this study the feature selection algorithm is implemented and tested using most popular dataset from Machine Learning Repository (UCI). The obtained results from experimental work confirmed that the proposed extension of the search neighbourhood including 4-Opt approach has provided better accuracy with suitable time than the Massudi methods. 2015 Thesis https://etd.uum.edu.my/5622/ https://etd.uum.edu.my/5622/1/s813731_01.pdf text eng public https://etd.uum.edu.my/5622/2/s813731_02.pdf text eng public masters masters Universiti Utara Malaysia [1] B. Yuce, D. T. Pham, M. S. 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