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 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.