Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid
Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However, stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for jo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Maxwell Scientific Publication Corp.
2015
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/17183/1/9.pdf |
| _version_ | 1825803832999804928 |
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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 | Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However,
stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing.The proposed approach is based on a high level hybridization.The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix.Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average
makespan values. |
| first_indexed | 2024-07-04T06:04:26Z |
| format | Article |
| id | uum-17183 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:04:26Z |
| publishDate | 2015 |
| publisher | Maxwell Scientific Publication Corp. |
| record_format | eprints |
| spelling | uum-171832016-04-27T01:05:50Z https://repo.uum.edu.my/id/eprint/17183/ Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana QA75 Electronic computers. Computer science Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However, stand-alone metaheuristic algorithms do not always show good performance in every problem instance. This study proposes a high level hybrid approach between ant colony system and genetic algorithm for job scheduling in grid computing.The proposed approach is based on a high level hybridization.The proposed hybrid approach is evaluated using the static benchmark problems known as ETC matrix.Experimental results show that the proposed hybridization between the two algorithms outperforms the stand-alone algorithms in terms of best and average makespan values. Maxwell Scientific Publication Corp. 2015 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/17183/1/9.pdf Alobaedy, Mustafa Muwafak and Ku-Mahamud, Ku Ruhana (2015) Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid. Research Journal of Applied Sciences, Engineering and Technology, 11 (7). pp. 806-816. ISSN 20407459 http://doi.org/10.19026/rjaset.11.2044 doi:10.19026/rjaset.11.2044 doi:10.19026/rjaset.11.2044 |
| spellingShingle | QA75 Electronic computers. Computer science Alobaedy, Mustafa Muwafak Ku-Mahamud, Ku Ruhana Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid |
| title | Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid |
| title_full | Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid |
| title_fullStr | Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid |
| title_full_unstemmed | Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid |
| title_short | Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid |
| title_sort | hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/17183/1/9.pdf |
| work_keys_str_mv | AT alobaedymustafamuwafak hybridantcolonysystemandgeneticalgorithmapproachforschedulingofjobsincomputationalgrid AT kumahamudkuruhana hybridantcolonysystemandgeneticalgorithmapproachforschedulingofjobsincomputationalgrid |