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基于混合免疫协同进化算法的模糊-PI优化控制 被引量:1

Fuzzy-PI optimization control based on hybrid immune co-evolutionary algorithm
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摘要 针对遗传算法诸如局部搜索能力差、早熟收敛、“退化”现象等问题,在协同进化算法(CA)的基础上融入传统的单纯形算法,同时引入免疫算子来防止“退化”现象,提出了混合免疫协同进化算法(HICA),并设计了一种自适应交叉、变异算子以提高算法的运算效率;应用HICA对模糊-PI控制器的各个参数进行协同优化,设计了体现控制器综合性能指标的目标函数,仿真结果表明:提出的基于HICA模糊-PI控制优化方法可以获得满意的控制效果. In view of such drawbacks of genetic algorithm as poor local search ability and premature convergence, a kind of hybrid immune co-evolutionary algorithm (HICA) was proposed by incorporating simplex algorithm into co-evolutionary algorithm (CA) and introducing immune theory into CA. And the adaptive crossover and mutation were adopted in order to improve the efficiency of the algorithm. The integrated optimal design method for a fuzzy-PI controller was presented, in which parameters could be optimized cooperatively. The simulation results show that the proposed fuzzy-PI control has satisfactory performances.
出处 《沈阳工业大学学报》 EI CAS 2007年第2期161-164,共4页 Journal of Shenyang University of Technology
关键词 协同进化 单纯形算法 免疫功能 模糊-PI控制 仿真 co-evolutionary simplex algorithm immune function fuzzy-PI control simulation
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  • 1Karr C L.Genetic algorithms for fuzzy controllers[J].Ai Expert,1991,6(2):26-33.
  • 2Karr C L,Gentry E J.Fuzzy control of pH using genetic algorithms[J].IEEE Transactions on Fuzzy Systems,1993,1 (1):46-53.
  • 3Homaifar A,Mc C E.Simultaneous design of membership functions and rule sets for fuzzy controllers using genetic algorithms[J].IEEE Transactions on Fuzzy System,1995,3(2):129-139.
  • 4Lim M H,Raharrdja S,Gwee B H.A GA parading for learning fuzzy rules[J].Fuzzy Sets and Systems,1996,82(2):177-186.
  • 5Hoffmann F.Evolutionary algorithm for fuzzy control system design[J].Proceedings of the IEEE,2001,89:1318-1333.
  • 6Rudolph G.Convergence analysis of canonical genetic algorithms[J].IEEE Trans on Neural Networks,1994,5(1):96-101.
  • 7Potter M A,Dejong K A.Cooperative co-evolution:an architecture for evolving co-adapted subcomponent[J].Evolutionary Computation,2000,8 (1):21-29.
  • 8孙增圻 张再兴 邓志东 等.智能控制理论与技术[M].北京:清华大学出版社,2002..

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  • 1ERICA Klarreich. Inspired by immunity[ J ]. Nature, 2002,415: 468 - 470.
  • 2Farmer J D,Packard N H, Perelson A S. The immune system, adaptation, and machine learning[ J ]. Physica D, 1986,22 (D) : 187 - 204.
  • 3Dasgupta D, Gonzalez F. An immunity-based technique to characterize inlrusions in computer networks[ J]. IEEE Transactions on Evolutionary Computation, 2002,6(3 ) : 156 - 162.
  • 4De Castro L. N, Von Zuben F J. Learning and optimization using the clonal selection principle [ J]. IEEE Transactions on Evolutionary Computation, 2002,6(3) :239 - 251.
  • 5Esponda F, Forrest S, Helman P. A formal framework for positive and negative detection[ J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2004, 34 ( 1 ) : 357 - 373.
  • 6Ehrilich P R, Raven P H. Butterflies and plants: a study in coevolution[ J]. Evolution, 1964,18(4) :586 - 608.
  • 7Jazen D H. When is it coevolution? [ J]. Evolution, 1980,34 (3) :611 - 612.
  • 8Rudolph G. Convergence analysis of canonical genetic algo-rithms[ J ]. IEEE Transactions on neural networks, 1994, 5 (1):96- 101.
  • 9Guo T,Michalewicz Z. Inver-over operator for the TSP[A]. FAben AE,et al,eds. Proc of the 5th Parallel Problem Solving from Nature Conf[ C ]. Nerlin: Springer-Verlag, 1998. 803 - 812.
  • 10戚玉涛,焦李成,刘芳.基于并行人工免疫算法的大规模TSP问题求解[J].电子学报,2008,36(8):1552-1558. 被引量:12

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