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...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2015
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| Online Access: | https://repo.uum.edu.my/id/eprint/15572/1/PID164.pdf |
| _version_ | 1825803559352926208 |
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| author | Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana |
| author_facet | Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana |
| author_sort | Alobaedy, Mustafa Muwafak |
| collection | UUM |
| 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. |
| first_indexed | 2024-07-04T05:59:06Z |
| format | Conference or Workshop Item |
| id | uum-15572 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:59:06Z |
| publishDate | 2015 |
| record_format | eprints |
| spelling | uum-155722016-04-27T01:07:01Z https://repo.uum.edu.my/id/eprint/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 https://repo.uum.edu.my/id/eprint/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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/15572/1/PID164.pdf |
| work_keys_str_mv | AT alobaedymustafamuwafak lowandhighlevelhybridizationofantcolonysystemandgeneticalgorithmforjobschedulingingridcomputing AT kumahamudkuruhana lowandhighlevelhybridizationofantcolonysystemandgeneticalgorithmforjobschedulingingridcomputing |