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Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing


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.

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ISBN No: 978-967-0910-02-4 Jointly organized by: Universiti Utara Malaysia & Istanbul Zaim University
Uncontrolled Keywords: grid computing, job scheduling, hybrid metaheuristic algorithm, ant colony system, genetic algorithm
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Computing
Depositing User: Prof. Dr. Ku Ruhana Ku Mahamud
Date Deposited: 01 Oct 2015 06:07
Last Modified: 27 Apr 2016 01:07
URI: http://repo.uum.edu.my/id/eprint/15572

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