Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing
Hybrid metaheuristic algorithms have the ability to produce better solution than stand-alone approach and no algorithm could be concluded as the best algorithm for scheduling algorithm or in general, for combinatorial problems.This study presents the low and high level hybridization of ant colony sy...
Saved in:
Main Authors: | , |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2015
|
Subjects: | |
Online Access: | http://repo.uum.edu.my/15572/1/PID164.pdf http://repo.uum.edu.my/15572/ http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uum.repo.15572 |
---|---|
record_format |
eprints |
spelling |
my.uum.repo.155722016-04-27T01:07:01Z http://repo.uum.edu.my/15572/ Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana QA75 Electronic computers. Computer science Hybrid metaheuristic algorithms have the ability to produce better solution than stand-alone approach and no algorithm could be concluded as the best algorithm for scheduling algorithm or in general, for combinatorial problems.This study presents the low and high level hybridization of ant colony system and genetic algorithm in solving the job scheduling in grid computing.Two hybrid algorithms namely ACS(GA) as a low level and ACS+GA as a high level are proposed.The proposed algorithms were evaluated using static benchmarks problems known as expected time to compute model. Experimental results show that ant colony system algorithm performance is enhanced when hybridized with genetic algorithm specifically with high level hybridization. 2015 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/15572/1/PID164.pdf Alobaedy, Mustafa Muwafak and Ku-Mahamud, Ku Ruhana (2015) Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey. http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
institution |
Universiti Utara Malaysia |
building |
UUM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Utara Malaysia |
content_source |
UUM Institutionali Repository |
url_provider |
http://repo.uum.edu.my/ |
language |
English |
topic |
QA75 Electronic computers. Computer science |
spellingShingle |
QA75 Electronic computers. Computer science Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing |
description |
Hybrid metaheuristic algorithms have the ability to produce better solution than stand-alone approach and no algorithm could be concluded as the best algorithm for scheduling algorithm or in general, for combinatorial problems.This study presents the low and high level hybridization of ant colony system and genetic algorithm in solving the job scheduling in grid computing.Two hybrid algorithms namely ACS(GA) as a low level and ACS+GA as a high level are proposed.The proposed algorithms were evaluated using static benchmarks problems known as expected time to compute model. Experimental results show that ant colony system algorithm performance is enhanced when hybridized with genetic algorithm specifically with high level hybridization. |
format |
Conference or Workshop Item |
author |
Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana |
author_facet |
Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana |
author_sort |
Alobaedy, Mustafa Muwafak |
title |
Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing |
title_short |
Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing |
title_full |
Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing |
title_fullStr |
Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing |
title_full_unstemmed |
Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing |
title_sort |
low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing |
publishDate |
2015 |
url |
http://repo.uum.edu.my/15572/1/PID164.pdf http://repo.uum.edu.my/15572/ http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
_version_ |
1644281751924637696 |
score |
13.18916 |