基于模糊粒子群优化的计算网格工作调度算法

网格计算是利用网络把分散的计算资源组织起来解决复杂问题的计算模式，工作调度是待解决的主要问题之一。本文提出一种基于模糊粒子群优化的网格计算工作调度算法，该算法利用模糊粒子群优化动态地产生网格计算工作调度的优化方案，使现有计算资源完成所有工作的时间最小化。实验结果表明，与基于遗传算法、模拟退火、蚁群算法的工作调度方法相比，所提出的算法在时间和精度上具有一定的优势。

Job Scheduling on Computational Grids Using Fuzzy Particle Swarm Optimization

Computational Grids are the computing framework to meet the growing computational demands, which re-organizes the resources of many computers in the networks to solve a large of complex problems. Essential grid services contain intelli- gent functional mechanism for discovery, publishing of resources as well as scheduling, submission and monitoring of jobs. The paper introduces a novel approach based on fuzzy Particle Swarm Optimization (PSO) for scheduling jobs on computational grids. Our approach is to dynamically generate an optimal schedule so as to complete the tasks in a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of our proposed approach with a direct Genetic Algorithm (GA), Simulated Annealing (SA) and Ant Colony Algorithm (ACO) approach. The results illustrated the poten- this of our approach, especially on the balance between time and precision.