%0 Journal Article
%T Job Scheduling on Computational Grids Using Fuzzy Particle Swarm Optimization<br>基于模糊粒子群优化的计算网格工作调度算法
%A WANG Xiu-Kun
%A CHENG Wen-Shu
%A LIU Hong-Bo
%A <br>王秀坤
%A 程文树
%A 刘洪波
%J 计算机科学
%D 2007
%I 
%X 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.
%K Grid computing
%K PSO
%K GA
%K SA
%K ACO<br>网格计算
%K 粒子群优化
%K 遗传算法
%K 退火算法
%K 蚁群算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=04C119E7854B6DCC4E3651D2C9C008C9&yid=A732AF04DDA03BB3&vid=339D79302DF62549&iid=708DD6B15D2464E8&sid=0401E2DB1F51F8DE&eid=5C3443B19473A746&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=11