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计算机工程 ›› 2009, Vol. 35 ›› Issue (11): 216-217,. doi: 10.3969/j.issn.1000-3428.2009.11.074

• 人工智能及识别技术 • 上一篇    下一篇

一种新的混沌差分进化算法

谭 跃1,2,谭冠政1,涂 立2   

  1. (1. 中南大学信息科学与工程学院,长沙 410083;2. 湖南城市学院物理与电信工程系,益阳 413000)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-06-05 发布日期:2009-06-05

Novel Chaos Differential Evolution Algorithm

TAN Yue1,2, TAN Guan-zheng1, TU Li2   

  1. (1. School of Information Science and Engineering, Central South University, Changsha 410083;
    2. Department of Physics and Telecom Engineering, Hunan City University, Yiyang 413000)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-06-05 Published:2009-06-05

摘要: 提出一种新的混沌差分进化(CDE)算法,在每一代中通过差分进化(DE)算法找到最佳个体,在最佳个体附近用混沌方法进行局部搜索,通过引入调节因子加强其搜索能力。6个基本测试函数的优化结果表明,当误差函数精度为10-14时,与DE相比,CDE的寻优能力更强、收敛速度较快。

关键词: 差分进化, 混沌, 局部搜索

Abstract: This paper proposes a new Chaos Differential Evolution(CDE) algorithm. It uses Differential Evolution(DE) algorithm to find the best individual each generation, then chaos based local search is executed nearby the best individual. A scaling factor is introduced to enhance the searching ability of CDE. Experimental results on six benchmark functions show the error function value is 10-14, both the ability of finding optimal solution and convergence speed using CDE are better than using DE.

Key words: Differential Evolution(DE), chaos, local search

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