计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (7): 41-43.

• 学术探讨 • 上一篇    下一篇

改进量子遗传算法用于多峰值函数优化

贺敏伟1,3,李贵海2,阮柏尧2,汪杨林2,林 健3   

  1. 1.广东商学院 信息学院,广州 510320
    2.五邑大学 信息学院,广东 江门 529020
    3.北京航空航天大学 经济管理学院,北京 100083
  • 收稿日期:2007-09-14 修回日期:2007-11-27 出版日期:2008-03-01 发布日期:2008-03-01
  • 通讯作者: 贺敏伟

Application of modified quantum genetic algorithm in optimization of multi-peak functions

HE Min-wei1,3,LI Gui-hai2,RUAN Bo-yao2,WANG Yang-lin2,LIN Jian3   

  1. 1.School of Information,Guangdong University of Business Studies,Guangzhou 510320,China
    2.School of Information,Wuyi University,Jiangmen,Guangdong 529020,China
    3.School of Economics & Management,Beihang University,Beijing 100083,China
  • Received:2007-09-14 Revised:2007-11-27 Online:2008-03-01 Published:2008-03-01
  • Contact: HE Min-wei

摘要: 传统遗传算法(SGA)在处理多峰值函数优化问题中存在局部收敛性的问题,最初的量子遗传算法(QGA)也存在这一问题。运用一种改进量子遗传算法(MQGA),有效地解决了一些多峰值函数的优化问题。根据几个重要的测试函数进行仿真实验结果证明,与SGA和QGA相比,改进的量子遗传算法(MQGA)在一些多峰值优化问题中更具有效性和可行性。

Abstract: Simple Genetic Algorithm(SGA) has some disadvantages in the multi-peak functions optimization.So does the Quantum Genetic Algorithm(QGA).In this paper,using a Modified Quantum Genetic Algorithm(MQGA) obtains the good solutions in some optimization of multi-peak functions.Compared with the SGA and the QGA,the test results for some important functions show that MQGA is more effective and feasible in some optimization of multi-peak functions.