期刊文献+

求解全局优化问题的遗传退火算法 被引量:13

Genetic-annealing algorithm for global optimization problems
下载PDF
导出
摘要 针对全局优化过程中,算法计算时间长、收敛时机不成熟、容易陷入局部最优等现象,在分析模拟退火算法和遗传算法优缺点的基础上提出了新的遗传退火混合算法,并将新的交叉、变异策略和诱导微调方法应用于算法中,通过10组非线性约束函数的测试表明,该算法能够在保持较高精度的前提下快速收敛。 Based on the analyzing of the simulate annealing algorithm and genetic algorithm,a genetic-annealing algorithm with new crossover strategy,mutation strategy and inducing adjustment strategy is proposed to solve the problems including taking long time,premature convergence,and easily trapping into local optimum value in the process of global optimization.This paper adopts ten typical test functions to experiment.The results demonstrate that the algorithm can converge quickly with high accuracy.
出处 《计算机工程与应用》 CSCD 北大核心 2007年第12期62-65,共4页 Computer Engineering and Applications
关键词 全局优化 遗传退火算法 交叉策略 变异策略 诱导微调 global optimization genetic-annealing algorithm crossover strategy mutation strategy inducing adjustment
  • 相关文献

参考文献8

  • 1刘习春,喻寿益.局部快速微调遗传算法[J].计算机学报,2006,29(1):100-105. 被引量:37
  • 2Cho S B.Combining modular neural networks developed by evolutionary algorithm[C]//Proceedings of the 1997 IEEE International Conference on Evolutionary Computation,Indianapolis,1997:647-650.
  • 3Zhao Q F,Arlo.Study on co-evolutionary learning of neural networks[M].Heidelberg:Springer-Verlag,1997.
  • 4Belew R,Booker L.Proceeding of the 4th International Conference on Genetic Algorithms[C[.Los Altos,CA:Morgan Kaufmann Publishers,1991.
  • 5Whitley D,Mathias K,Fitzhorn P.Delta coding:An iterative search strategy for genetic algorithms[M].Los Altos:Morgan Kaufmann Publishers,1991:74-84.
  • 6王雪梅,王义和.模拟退火算法与遗传算法的结合[J].计算机学报,1997,20(4):381-384. 被引量:123
  • 7周丽,黄素珍.基于模拟退火的混合遗传算法研究[J].计算机应用研究,2005,22(9):72-73. 被引量:36
  • 8周丽,孙树栋.遗传算法原理及其应用[M].北京:国防工业出版社,2001.

二级参考文献16

  • 1Rowlins G. ed.. Foundations of Genetic Algorithm. Los Altos: Morgan Kanfmann, 1991.
  • 2Powll D. , Tong S. , Skolnik M.. Domain independent machine for design optimization. In: Proceedings of the AAAI-90,George Mason University, USA, 1989, 151-159.
  • 3Cho S. B.. Combining modular neural networks developed by evolutionary algorithm. In: Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, Indianapolis, 1997, 647-650.
  • 4Zhao Q. F. , Arlo, Study on Co-evolutionary Learning of Neural Networks. Heidelberg: Springer-Verlag, 1997.
  • 5Michalewicz Z. et. al. eds.. In: Proceeding of the 1st International Conference on Evolutionary Computation (ICEC' 94),Orlando, Florida, USA, 1994, 665-669.
  • 6Goldberg D. E.. Real-coded genetic algorithms, virtual alphabets, and blocking. University of Illinois at Urbana-Champaign: Technical Report No. 90001,1990.
  • 7Holland J. H.. Adaptation in Natural and Artificial Systems.Ann Arbor: The University of Michigan Press, 1975.
  • 8Belew R. , Booker L.. Proceedings of the 4th International Conference on Genetic Algorithms. Los Altos, CA: Morgan Kaufmann Publishers, 1991.
  • 9Whitley D. , Mathias K. , Fitzhorn P.. Delta Coding: An Iterative Search Strategy for Genetic Algorithms. Los Altos, Morgan Kaufmann Publishers, 1991, 77-84.
  • 10Michalewicz Z.. Genetic Algorithms+ Delta Strucures= Evolution Programs. Berlin Heidelberg: Springer-Verlag, 1996.

共引文献190

同被引文献100

引证文献13

二级引证文献44

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