| A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling |
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| 引用本文： | SHU Wanneng,ZHENG Shijue~ Department of Computer Science,Huazhong Normal University,Wuhan 430079,Hubei,China. A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling[J]. 武汉大学学报:自然科学英文版, 2006, 11(5): 1378-1382. DOI: 10.1007/BF02829270 |
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| 作者姓名： | SHU Wanneng ZHENG Shijue~ Department of Computer Science Huazhong Normal University Wuhan 430079 Hubei China |
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| 作者单位： | SHU Wanneng，ZHENG Shijue~ Department of Computer Science，Huazhong Normal University，Wuhan 430079，Hubei，China |
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| 基金项目： | 国家重点基础研究发展计划(973计划) |
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| 摘 要： | 0 IntroductionGrid computingis a hot topic inthe current internet research,and a developing direction of the parallel and distributedprocess[1 ,2].Since the task scheduling in grid computing faces aNP-hard problem[3];it has drawn attention from many scholarsand become the focusinthe field of the current grid computing re-search.In recent years , two global random and opti mal algorithmhave been widelystudiedandappliedinthefield of the gridcompu-ting research: GA(Genetic Algorithm) and SA( …
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| 收稿时间： | 2006-02-28 |
A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling |
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| SHU Wanneng,ZHENG Shijue. A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling[J]. Wuhan University Journal of Natural Sciences, 2006, 11(5): 1378-1382. DOI: 10.1007/BF02829270 |
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| Authors: | SHU Wanneng ZHENG Shijue |
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| Affiliation: | (1) Department of Computer Science, Huazhong Normal University, 430079 Wuhan, Hubei, China |
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| Abstract: | In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGSAHA) and applied to solve task scheduling problem in grid computing .It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively. When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole. From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing. |
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| Keywords: | grid computing task scheduling genetic algorithm simulated annealing PGSAHA algorithm |
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