广东工业大学学报 ›› 2011, Vol. 28 ›› Issue (4): 34-37.

• 综合研究 • 上一篇    下一篇

基于最速方向搜索的混合遗传算法

  

  1. 广东工业大学 应用数学学院,广东 广州 510006
  • 出版日期:2011-12-25 发布日期:2011-12-25
  • 作者简介:涂井先(1987-),男,硕士研究生,主要研究方向为智能计算.

A Hybrid Genetic Algorithm Based on the Search Algorithm of the Steepest Direction

  1. A of Applied Mathematics, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2011-12-25 Published:2011-12-25

摘要: 针对目标函数复杂求导困难或目标函数不可导的优化问题,给出了最速方向搜索方法,它在搜索过程不需要导数信息,克服了一些局部搜索算法需要求出导数的缺点.在此基础上提出了基于最速方向搜索的混合遗传算法,将最速方向搜索算法与遗传算法有效结合,增强了遗传算法的搜索能力.数值实验证明,改进后的遗传算法性能优于当前一些较好的优化算法.

关键词: 遗传算法;最速方向;局部搜索;适应值共享

Abstract: The search algorithm of the steepest direction is proposed to solve the optimization problem that the objective function is complex and the derivative is hard or impossible to obtain, The algorithm doesn’t need to obtain the derivative in its searching process, overcoming the weakness of needing to acquire the derivative during their searching process, which some other local search algorithms have. A hybrid genetic algorithm, based on the search algorithm of the steepest direction, is proposed. The algorithm combines the search algorithm of the steepest direction with the genetic algorithm effectively, enhancing the searching ability of the genetic algorithm. The experimental results show that the improved genetic algorithm is more effective than some current optimization algorithms.

Key words: genetic algorithm; steepest direction; local search; fitness sharing

[1] Tu Chengyuan,Zeng Yanjun.A new genetic algorithm based upon globallyoptimal choosing and its practices[J].Engineering Science,2003,5(2):28-29.

[2] Ming Zhou,Sun Shudong.Principle of Genetic Algorithmn and Applicatio[M].Beijing:National Defence Industry Press,1999.

[3] Zhao Chuanxin, Ji Yimu.Particle swarm optimization for 0/1 knaps problem[J].Microcomputer Develepment,2005(10):23-25.

[4] 周明, 孙树栋. 遗传算法原理及其应用[M].北京:国防工业出版社,1999.

[5] 刘伟,刘海林.基于外点法的混合遗传算法求解约束优化问题[J].计算机应用,2007,27(1):238-240.

[6] 白向军,彭国华,陈晓.基于遗传算法和最速下降法的B6zier曲线拟合[J].计算机工程与设计,2009,30(1):194-196.

[7] 张晓伟,邢志栋,董建民.求解一类无约束优化的混合遗传算法[J].西北大学学报:自然科学版,2005,35(2):130-132.

[8] 江中央,蔡自兴,王勇.求解全局优化问题的混合自适应正交遗传算法[J].Journal of Software,2010,21(6):1296-1307.

[9] 唐焕文,秦学志.实用最优化方法[M].3版.大连:大连理工大学出版社,1999.

[10] Holland J H.Adaptation in Natural and Artificial Systems[M].Ann Arbor:The MIT Press,1992.

[11] Goldberg D E, Richarson.Genetic algorithms with sharing for multimodal function optimization[C]∥Grefenstette J ed.Proc 2nd Int Conf Genetic Algorithms and Their Applications.Hillsdale,NJ:Lawrence Erlbaum,1987,41-49.

[12] Kusum Deep,Manoj Thakur.A New Crossover Operator for Real Coded Genetic Algorithms[M].India:Elsevier Inc,2007.

[13] Tu Zhenguo,Lu Yong.A Robust stochastic genetic algorithm for global numerical optimization[J].Evolutionary Computation,IEEE Transactions on,2004,8(5):456-470.

[14] Yao Xin,Liu Yong,Lin Guangmin.Evolutionary programming made faster[J]. Evolutionary Computation,IEEE Transactions on,1999,3(2):82-102.

[15] Wang Y P,Dang C Y.An evolutionary algorithm for global optimization based on leve1-set evolution and Latin squares[J].Evolutionary Computation,IEEE Transactions on,2007,11(5):579-595.
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!