Grid Computing Job Scheduling Using Biogeography based Optimization with Elitism
Sung-Soo Kim · 한국경영과학회지 · 2019
Job scheduling in grid resource management is a complex NP-complete problem of computational grids. The objective and contribution of this research are to optimize and propose the discrete job scheduling of grid computation by using biogeography-based optimization (BBO) with elitism as the meta-heuristic. The migration of converged search and mutation toward a diversified search is used to change the current solutions and adapt new good solutions by keeping the fine solutions from elitism. BBO is an adaptive process, whereas genetic algorithm and other heuristic algorithms are reproductive processes. Simulation results show that the performance of our proposed BBO with suitable elitism and mutation rate is better than those of early methods (genetic algorithm, simulated annealing, particle swarm optimization, and group search optimizer) in job scheduling benchmark problems.