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求解0-1动态优化问题的双概率原对偶遗传算法 被引量：3

Double-probability primal-dual genetic algorithm for 0-1 dynamic optimization problems
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摘要 在原对偶遗传算法(PDGA)的基础上,提出一种双概率原对偶遗传算法(DPPDGA).引入弱势基因位值与强势基因位值的概念,对二者赋予不同的对偶映射概率,并对两个对偶概率进行适应性调整.比较原始算法,改进算法使种群具有更理想的多样性,并利于种群较快地收敛到满意解.仿真结果表明,该算法在0-1动态优化问题的求解中具有更好的性能. On the basis of primal-dual genetic algorithm （PDGA）, a new double-probability-based primal-dual（DPPDGA） genetic algorithm is proposed for 0-1 dynamic optimization problems. The concepts of inferior-allele and superior-allele are introduced, which are given different probabilities respectively for dual mapping, and they are adjusted adaptively. The algorithm has improvement on keeping diversity and guaranteeing convergence rate of population. The numerical experiment results show that the proposed algorithm has better performance for solving 0-1 dynamic optimization problems than that of PDGA.
作者 刘黎黎 汪定伟 王洪峰
机构地区 东北大学信息科学与工程学院
出处 《系统工程学报》 CSCD 北大核心 2009年第5期636-640,共5页 Journal of Systems Engineering
基金 国家自然科学基金资助项目(70931001 70771021) 创新群体资助项目(60821063) 国家教育部博士点基金资助项目(200801450008)
关键词 遗传算法 动态优化 双概率 原对偶映射 genetic algorithm dynamic optimization double-probability primal-dual mapping
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献10

  • 1Ryan C. Diploidy without dominance [ A]// Proceedings of 3rd Nordic Workshop Genetic Algorithms [ C]. Washington, USA : IEEE Service Center, 1997 : 63 - 70.
  • 2Branke J, Kaubler T, Schmidt C, et al. A multi-population approach to dynamic optimization problems [ A ]// Adaptive Computing in Design and Manufacturing 2000[M ]. Berlin : Springer-Verlag, 2000.
  • 3Oppacher F, Wineberg M. The shifting balance genetic algorithm : Improving the GA in a dynamic environment [ A ]// The Proceedings of the Genetic and Evolutionary Computation Conference [ C]. Washington, USA: IEEE Service Center, 1999: 504 -510.
  • 4Yang S. Non-stationary problem optimization using the primal-dual genetic algorithm [ A ]// Proceedings of the IEEE Congress on Evolutionary Computation[ C]. Chicago: IEEE Service Center, 2003: 2246- 2253.
  • 5Collard P, Escazut C. An evolutionary approach for time dependant optimization[ J]. International Journal on Artificial Intelligence Tools, 1997, 6(4) : 665 -695.
  • 6王洪峰,汪定伟,杨圣祥.动态环境中的进化算法[J].控制与决策,2007,22(2):127-131. 被引量：18
  • 7Morrison R W, De Jong K A. Triggered hypermutation revisited [ A ]// Proceedings of the IEEE Congress on Evolutionary Computation[ C ]. Washington, USA: IEEE Service Center, 2000 : 1025 - 1032.
  • 8Branke J. Memory Enhanced evolutionary algorithms for changing optimization problems [ A ]//Proceedings of the IEEE Congress on Evolutionary Computation[ C]. Washington, USA: IEEE Service Center, 1999:1875 -1882.
  • 9Wang H, Wang D. An improved primal-Dual genetic algorithm for optimization in dynamic environments[ A]//Processings of 13th International Conference on Neural Information, Part Ⅲ[ C ]. Berlin, Germany: Springer-Verlag, 2006:836 -844.
  • 10Yang R, Douglas I. Simple genetic algorithm with local tuning: efficient global optimizing technique [ J ]. Journal of Optimization Theory and Applications, 1998, 98 (2) : 449 -465.

二级参考文献42

  • 1Gerratt S M,Walker J H.Genetic algorithms:Combining evolutionary and 'non'-evolutionary methods in tracking dynamic global optima[C].Proc of the Genetic and Evolutionary Computation Conf.San Francisco:Morgan Kaufmann Publishers,2002:359-366.
  • 2Simoes A,Costa E.Using GAs to deal with dynamic environments:A comparative study of several approaches based on promoting diversity[C].Proc of the Genetic and Evolutionary Computation Conf.San Francisco:Morgan Kaufmann Publishers,2002:698.
  • 3Tin'os R,Carvalho A.A genetic algorithm with gene dependent mutation probability for non-stationary optimization problems[C].Proc of 2004 Congress on Evolutionary Computing.Piscataway:IEEE Service Center,2004:1278-1285.
  • 4Grefenstette J J.Genetic algorithms for changing environments[C].Parallel Problem Solving from Nature.Brussels,1992:137-144.
  • 5Cobb H G,Grefenstette J J.Genetic algorithms for tracking changing environments[C].Proc of 5th Int on Genetic Algorithms.San Francisco:Morgan Kaufmann Publishers,1993:523-530.
  • 6Eriksson R,Olsson B.On the performance of evolutionary algorithms with life-time adaptation in dynamic fitness landscapes[C].Proc of 2004 Congress on Evolutionary Computing.Piscataway:IEEE Service Center,2004:1293-1300.
  • 7Andersen H C.An investigation into genetic algorithms,and the relationship between speciation and the tracking of optima in dynamic functions[D].Brisbane:Queensland University of Technology,1991.
  • 8Hocaoglu C,Sanderson A C.Planning multi-paths using speciation in genetic algorithms[C].Proc of the 3rd IEEE Int Conf on Evolutionary Computation.Piscataway:IEEE Service Center,1996:378-383.
  • 9Mori N,Kita H,Nishikawa Y.Adaptation to a changing environment by means of the thermodynamical genetic algorithm[C].Parallel Problem Solving from Nature.Berlin:Springer Publishers,1996:513-522.
  • 10Collard P,Escazut C,Gaspar A.An evolutionary approach for time dependant optimization[J].Int J on Artificial Intelligence Tols,1997,6(4):665-695.

共引文献17

同被引文献30

  • 1武晓今,朱仲英.遗传算法多样性测度问题研究[J].信息与控制,2005,34(4):416-422. 被引量：17
  • 2Tin'os R, Carvalho A. A genetic algorithm with gene dependent mutation probability for nonstationary optimization problems[C]// Proceedings of the Congress on Evolutionary Computing. San Diego: IEEE Press, 2004:1278 - 1285.
  • 3Wang H, Wang D. An improved primal-dual genetic algorithm for optimization in dynamic environments[C]//Proceedings of the 13th International Conference on Neural Information Processing. Hong Kong: Springer, 2006: 835-844.
  • 4Simoes A, Costa E. Variable-size memory evolutionary algorithm to deal with dynamic environments[C]//Proceedings of EvoWork- shops 2007 on Applications of Evolutionary Computing. Valencia, Spain: Springer, 2007:617 - 626.
  • 5Yang S. Population-based incremental learning with memory scheme for changing environments[C]//Proceedings of the Genetic and Evolutionary Computation Conference. Washington: ACM Press, 2005:711 - 718.
  • 6Jahne M, Li X D, Branke J. Evolutionary algorithms and multi-objectivizafion for the travelling salesman problem[C]//Proceedings of the Genetic and Evolutionary Computation Conference. Montreal: ACM Press, 2009:595 - 602.
  • 7Bui L T, Nguyen M H, Branke J, et al. Tackling dynamic problems with multiobjective evolutionary algorithms[M]//Multiobjective Problem Solving from Nature: From Concepts to Application. Berlin: Springer, 2007: 77-91.
  • 8Knowles J, Came U. Reducing local optima in single objective problems by multi-objectivization[C] // Proceedings of the First Conference on Evolutionary Multi-Criterion Optimization. Zurich: IEEE Press, 200 h 269 - 283.
  • 9Abbass H A, Deb K. Searching under multi-evolutionary pressures[C] //Proceedings of the Fourth Conference on Evolutionary Multi-Criterion Optimization. Spain: Springer, 2003:391 -404.
  • 10Yamasaki K. Dynamic Pareto Optimum GA Against the Changing Environments[M]//Branke J, Back T. Evolutionary Algorithms for Dynamic Optimization Problems, San Francisco: Springer, 2001: 47-50.

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系统工程学报
2009年 第5期

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