基于改进遗传算法的网格任务调度研究

网格任务调度是一个NP完全问题,它关注大规模的资源和任务调度,要求采用具有高效性的调度算法.提出了一种基于改进遗传算法的网格任务调度算法,在算法初始化种群产生时引入min-min算法和max-min算法,从而提高初始化种群的质量;算法迭代过程中采用了一种新的局部收敛判断以及改进的变异操作来防止局部收敛.仿真结果表明,该改进算法能更有效地解决网格任务调度问题.

Grid Task Scheduling Based on Improved Genetic Algorithm

Grid task scheduling is a NP-complete problem which concerns the scheduling of tasks and resources in a large scale, and thus a scheduling algorithm of high efficiency is rectuired. A grid task scheduling algorithm based on GA was proposed. In the process of population initialization, a new method which combines the min-min algorithm and the max-min algorithm was addressed, and in the evolution of the population a new criterion predicting the premature convergence was presented and the corresponding improved mutation was designed to avoid premature convergence. The simulation results show that this improved algorithm can solve the problem of grid task scheduling more effectively.