earticle

영어

A hybrid genetic scheduling strategy (H-GA) is described in this article, H-GA combines
with grouping and load balancing strategy based on traditional genetic algorithm (GA).
First, tasks are divided into several different subgroups by task granularity. Then, task
subgroup which is selected by granularity from big to small is used to schedule by the
genetic algorithm, and during scheduling, the load balancing strategy is used to adjust task
distribution in the individual. Grouping can cut down the length of individual, which speeds
up convergence of genetic algorithm. Load balancing strategy can make the individual better,
which also speeds up convergence of genetic algorithm. The implementation shows that
converging speed of H-GA is faster than GA, and result of H-GA is optimal than GA if the
iteration times are equal.