A hybrid differential evolution algorithm for meta-task scheduling in grids  

作　　者：康钦马 Jiang Changiun He Hong Huang Qiangsheng 

机构地区：[1]The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, P.R. China [2]School of Infomlation Engineering, Shandong University at Weihai, Weihai 264209, P.R. China

出　　处：《High Technology Letters》2009年第3期261-266,共6页高技术通讯（英文版）

基　　金：supported by the National Basic Research Program of China(No.2007CB316502);the National Natural Science Foundation of China(No.60534060)

摘　　要：Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re-sources in the grid.This paper presents a new hybrid differential evolution(HDE)algorithm for findingan optimal or near-optimal schedule within reasonable time.The encoding scheme and the adaptation ofclassical differential evolution algorithm for dealing with discrete variables are discussed.A simple but ef-fective local search is incorporated into differential evolution to stress exploitation.The performance of theproposed HDE algorithm is showed by being compared with a genetic algorithm(GA)on a known staticbenchmark for the problem.Experimental results indicate that the proposed algorithm has better perfor-mance than GA in terms of both solution quality and computational time,and thus it can be used to de-sign efficient dynamic schedulers in batch mode for real grid systems.Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous re- sources in the grid. This paper presents a new hybrid differential evolution （HDE） algorithm for finding an optimal or near-optimal schedule within reasonable time. The encoding scheme and the adaptation of classical differential evolution algorithm for dealing with discrete variables are discussed. A simple but ef- fective local search is incorporated into differential evolution to stress exploitation. The performance of the proposed HDE algorithm is showed by being compared with a genetic algorithm （GA） on a known static benchmark for the problem. Experimental results indicate that the proposed algorithm has better perfor- mance than GA in terms of both solution quality and computational time, and thus it can be used to design efficient dynamic schedulers in batch mode for real grid systems.

关 键 词：Hybrid differential evolution grid computing task scheduling genetic algorithm 

分 类 号：TM73[电气工程—电力系统及自动化]