Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (5): 1333-1335.DOI: 10.11772/j.issn.1001-9081.2015.05.1333

Previous Articles     Next Articles

Naïve differential evolution algorithm

WANG Shenwen1,2, ZHANG Wensheng2, QIN Jin3, XIE Chengwang4, GUO Zhaolu5   

  1. 1. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang Hebei 050031, China;
    2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
    3. School of Computer, Guizhou University, Guiyang Guizhou 550025, China;
    4. School of Software, East China Jiaotong University, Nanchang Jiangxi 330013, China;
    5. School of Science, Jiangxi University of Science and Technology, Ganzhou Guangdong 34100, China
  • Received:2014-12-29 Revised:2015-02-06 Online:2015-05-10 Published:2015-05-14


汪慎文1,2, 张文生2, 秦进3, 谢承旺4, 郭肇禄5   

  1. 1. 石家庄经济学院 信息工程学院, 石家庄 050031;
    2. 中国科学院 自动化研究所, 北京 100190;
    3. 贵州大学 计算机学院, 贵阳 550025;
    4. 华东交通大学 软件学院, 南昌 330013;
    5. 江西理工大学 理学院, 江西 赣州 341000
  • 通讯作者: 汪慎文
  • 作者简介:汪慎文(1979-),男,湖北红安人,副教授,博士,CCF会员,主要研究方向:智能计算、机器学习; 张文生(1966-),男,河南郑州人,研究员,博士生导师,博士,主要研究方向:模式识别、机器学习; 秦进(1978-),男,贵州黔西人,副教授,博士,主要研究方向:智能计算;谢承旺(1974-),男,湖北武汉人,副教授,博士,主要研究方向:智能计算; 郭肇禄(1984-),男,江西南康人,讲师,博士,主要研究方向:智能计算、并行计算.
  • 基金资助:



In order to solve singleness of mutation study, a naïve mutation strategy was proposed to approach the best individual and depart the worst one. So, a scale factor self-adaptation mechanism was used and the parameter was set to a small value when the dimension value of three random individuals is very close to each other, otherwise, set it to a large value. The results showed that the Differential Evolution (DE) with the new mechanism exhibits a robust convergence behavior measured by average number of fitness evaluations, successful running rate and acceleration rate.

Key words: Differential Evolution (DE), naï, ve mutation operator, scale factor, integrated evolution



关键词: 差分进化, 朴素变异算子, 缩放因子, 集成进化

CLC Number: