期刊文献+

分布式进化算法的性能测试与分析 被引量:1

Performance tests and analysis of distributed evolutionary algorithms
下载PDF
导出
摘要 针对分布式进化算法设计过程中由于缺乏对性能影响因素的分析而导致算法无法达到预期加速比的问题,提出一种全面的性能分析方法。根据分布式进化算法的组成结构,将影响分布式进化算法性能的因素分为进化操作开销、适应值计算开销和通信开销三个部分。首先研究进化算法在不同个体编码维数下进化操作开销的特性;其次,在进化操作开销相对固定的情况下,通过使用操作系统的延时函数控制适应值计算开销,通过改变个体编码维数控制通信开销;最后,应用控制变量方法,逐一测试各因素对算法加速比的影响。实验结果展现了三种因素的相互制约关系,给出了分布式进化算法获得更好加速比的条件。 Due to the lack of performance analysis while designing a distributed Evolutionary Algorithm( dEA), the designed algorithm cannot reach the expected speedup. To solve this problem, a comprehensive performance analysis method was proposed. According to the components of dEAs, factors that influence the performance of dEAs can be divided into three parts, namely, evolutionary cost, fitness evaluation cost and communication cost. Firstly, the feature of evolutionary cost under different individual encoding lengths was studied. Then when the evolutionary cost was kept unchanged, the fitness evaluation cost was controlled by using the delay function of the operating system and the communication cost was controlled by changing the length of individual encoding. Finally, the effect of each factor was tested through control variable method. The experimental results reveal the constraint relation among the three factors and point out the necessary conditions for speeding up dEAs.
出处 《计算机应用》 CSCD 北大核心 2014年第11期3086-3090,共5页 journal of Computer Applications
基金 国家自然科学基金资助项目(U1201258)
关键词 分布式进化算法 分布式模型 遗传算法 粒子群优化算法 性能分析 distributed Evolutionary Algorithm(dEA) distributed model Genetic Algorithm(GA) Particle Swarm Optimization(PSO) performance analysis
  • 相关文献

参考文献13

  • 1唐天兵,韦凌云,谢祥宏,严毅.分布式并行计算环境下混合遗传算法的研究[J].计算机工程与应用,2011,47(9):207-209. 被引量:4
  • 2苗启广,孔哲鹏,王艳红.基于分布式免疫进化算法的函数优化[J].系统工程与电子技术,2012,34(2):413-417. 被引量:2
  • 3LASSIG J, SUDHOLT D. The benefit of migration in parallel evolutionary algorithms[C] // GECCO 2010: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation. New York: ACM Press, 2010: 1105-1112.
  • 4LI C, WANG J, YAN X, et al.Subpopulation diversity based accepting immigrant in distributed evolutionary algorithms[C] // ICPADS 2013: Proceedings of the 2013 International Conference on Parallel and Distributed Systems. Piscataway: IEEE Press, 2013:422-423.
  • 5LASSIG J, SUDHOLT D. Adaptive population models for offspring populations and parallel evolutionary algorithms[EB/OL]. [2013-10-10]. http://www.icsi.berkeley.edu/pubs/algorithms/adaptivepopulationmodels11.pdf.
  • 6WHITLEY D. A genetic algorithm tutorial[J]. Statistics and Computing, 1994, 4(2): 65-85.
  • 7BRATTON D, KENNEDY J. Defining a standard for particle swarm optimization[C] // SIS 2007: Proceedings of the 2007 Swarm Intelligence Symposium. Piscataway: IEEE Press,2007: 120-127.
  • 8ALBA E, TOMASSINI M. Parallelism and evolutionary algorithms[J]. IEEE Transactions on Evolutionary Computation, 2002, 6 (5):443-462.
  • 9VITELA J E, CASTANOS O, A real-coded niching memtic algorithm for continuous multimodal function optimization[C] // CEC 2008: Proceedings of the 2008 IEEE Congress on Evolutionary Computation. Piscataway: IEEE Press,2008: 2170-2177.
  • 10ZHAN Z, ZHANG J, LI Y, et al.Adaptive particle swarm optimization [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2009,39(6):1362-1381.

二级参考文献20

  • 1徐宗本,高勇.遗传算法过早收敛现象的特征分析及其预防[J].中国科学(E辑),1996,26(4):364-375. 被引量:99
  • 2Tanese R, Holland J H, Stout Q F. Distributed genetic algo- rithms for function optimization[D]. USA: University of Michi- gan Ann Arbor, 1989.
  • 3Dasgupta D, Yua S H, Nino F. Recent advances in artificial immune systems: models and applications [J]. Applied Soft Computing ,2011,11(2) : 1574 - 1587.
  • 4Li F C, Xu L D, Jin C X, et al. Intelligent bionic genetic algo- rithm (IB-GA) and its eonvergence[J]. Expert Systems with Applications ,2011,38(7) :8804 - 8811.
  • 5Kumar R, Gill S. Premature convergence and genetic algorithm under operating system process scheduling problem[J]. Global Research in Computer Science, 2010,1 (5) : 1 - 5.
  • 6Abo-Zahhad M,Ahmed S M,Sabor N. The convergence speed of single- and multi-objective immune algorithm based optimization problems[J]. Signal Processing ,2010,4(5):247 - 303.
  • 7孙梦楠.改进免疫遗传算法在函数优化中的应用研究[D].江苏:苏州大学,2010.
  • 8Hu T, Harding S, Banzhaf W. Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm[J]. Genetic Programming and Evolvable Machines, 2010,11(2):205 -225.
  • 9Kang L S, Chen Y P. Parallel evolutionary algorithms and applica- tions[D]. Wuhan:China University of Geosciences,2008.
  • 10Tomassini M, Vanneschi L. Special issue on parallel and dis- tributed evolutionary algorithms [J]. Genetic Programming and Evolvable Machines, 2009,10(4) : 339 - 341.

共引文献4

同被引文献6

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部