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人工鱼群算法与差分进化变异蟑螂算法动态融合及其在网格任务调度中的应用 被引量:3

DYNAMIC FUSION OF ARTIFICIAL FISH-SWARM ALGORITHM AND COCKROACH SWARM OPTIMISATION WITH DIFFERENTIAL EVOLUTION MUTATION AND ITS APPLICATION IN GRID TASK SCHEDULING
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摘要 深入分析人工鱼群算法和蟑螂算法的特点基础,提出一种改进式蟑螂算法。将差分进化变异因子、禁忌表分别引入到蟑螂算法,加快了算法的搜索速度和获得全局最优解的能力。采用权衡种群中最优个体和精英个体之间的差异度的方式将改进后的蟑螂算法和人工鱼群算法动态融合。仿真实验表明将这种动态融合后的算法解决网格任务调度问题可以获得较好的调度效果。 In this paper,according to the analysis of characteristics of artificial fish swarm algorithm(AFSA) and cockroach swarm optimisation,an improved cockroach swarm optimisation is presented.Because of the introduction of differential evolution mutation and taboo table,the algorithm's searching speed and global search ability are all improved.The dynamic fusion of the AFSA and the improved cockroach swarm optimisation is realised in the way of measuring the difference between the optimal individual and the elite individual in the population.Simulative experiment shows that such algorithm after the dynamic fusion can achieve better scheduling effect when applying it in grid task scheduling.
出处 《计算机应用与软件》 CSCD 北大核心 2012年第5期175-177,233,共4页 Computer Applications and Software
关键词 网格 任务调度 人工鱼群算法 蟑螂算法 差分进化 Grid Task scheduling Artificial fish-swarm algorithm Cockroach swarm optimisation Differential evolution
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参考文献6

  • 1Laforenza D. Grid programming:some indications where we are headed author[ J]. Parallel Computing,2002,28( 12 ) : 1733 - 1752.
  • 2Lee Wang,Howard Jay Siegel,Vwani P Roychowdhury,et al. Task matc- hing and scheduling in heterogeneous computing environmentsusing a ge- netic-algorithm-based approach[ J ]. Journal of Parallel and Distributed Computing, 1997,47 ( 1 ) :8 - 22.
  • 3Dorigo M ,Stzle T. Ant Colony Optimization[ M]. MIT Press,Cambridge, 2004.
  • 4李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(11):32-38. 被引量:884
  • 5程乐.新的仿生算法:蟑螂算法[J].计算机工程与应用,2008,44(34):44-46. 被引量:12
  • 6Storn R, Price K. Differential evolution-a simple and efficient adap - tive scheme for global optimization over continuous spaces, TR - 95 - 012[ R ]. Berkeley : International Computer Science Institute, 1995.

二级参考文献11

  • 1戴汝为 周登勇.智能控制与适应性.第三届全球智能控制与自动化大会(WCICA'2000)[M].合肥:-,2000.11-17.
  • 2Colorni A,Dorigo M,Maniezzo V,et al.Distributed optimization by ant colonies[C]//Proc of the 1st European Conference on Artificial Life.Amsterdam:Elsevier Publisling, 1991 : 134-142.
  • 3Dogigo M.Optimization,learning and natural algorithms[D].Italy:Polltecnico diMilano, 1992.
  • 4Hendlass T.Preserving diversity in particle swarm optimization[M]. Lecture Notes in Computer Science,2003,2718:4104-4108.
  • 5Kennedy J,Eberhart R C.A discrete binary version of the particle swarm algofithm[J].Proceedings of IEEE Conferenceon Systems, 1997, 5:4104-4108.
  • 6Eberhart R, Kennedy J.A new optimizer using particles swarm theory[C]//Roc Sixth International Symposium on Micro Machine and Human Science,Nagoya,Japan.Piscataway:IEEE Service Center, 1995: 39-43.
  • 7Halloy J.Individual discrimination capability and collective decision-making[J].Journal of Theoretical Biology,2006,239:313-323.
  • 8吴庆洪,张纪会,徐心和.具有变异特征的蚁群算法[J].计算机研究与发展,1999,36(10):1240-1245. 被引量:306
  • 9李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(11):32-38. 被引量:884
  • 10李晓磊,钱积新.基于分解协调的人工鱼群优化算法研究[J].电路与系统学报,2003,8(1):1-6. 被引量:137

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