基于自适应遗传算法的网格任务调度优化

Grid Task Scheduling Optimization Based on Adaptive Genetic Algorithm

针对传统任务调度算法效率较低、资源负载不平衡等缺点,基于遗传算法,考虑现代网格系统异构性和动态性的特点,提出一种有效的交叉概率和变异概率自适应更新方法,提高遗传算法的全局搜索能力和收敛速度.仿真实验表明,改进后的遗传算法在进化速度上有明显提升,可较好地处理网格任务调度问题,提高任务调度效率,降低资源负载的不平衡性.

In consideration of low efficiency,resource load imbalance of the traditional task scheduling algorithm and other shortcomings,and the heterogeneous and dynamic characteristics of a modern grid system,an effective adaptive update method for the crossover and mutation probability was proposed based on the genetic algorithms,which improves the global search ability and convergence speed of the genetic algorithm.Simulation results show that improved genetic algorithm enhances the speed of evolution significantly,can better handle grid task scheduling,improve task scheduling efficiency and reduce resource load imbalance with high practicality.