J4 ›› 2015, Vol. 42 ›› Issue (1): 75-81.doi: 10.3969/j.issn.1001-2400.2015.01.012

• 研究论文 • 上一篇    下一篇



  1. (1. 西安电子科技大学 计算机学院,陕西 西安  710071;
    2. 中国电子科技集团公司第五十四研究所,河北 石家庄  050081;
    3. 陕西省军区司令部 指挥自动化站,陕西 西安  710061)
  • 收稿日期:2013-04-14 出版日期:2015-02-20 发布日期:2015-04-14
  • 通讯作者: 姜建国
  • 作者简介:姜建国(1956-),男,教授.E-mail:jgjiang@mail.xidian.edu.cn.
  • 基金资助:


Adaptive bacterial foraging optimization algorithm

JIANG Jianguo1;ZHOU Jiawei1;ZHENG Yingchun1,2;WANG Tao3   

  1. (1. School of Computer Science and Technology, Xidian Univ., Xi'an  710071, China;
    2. The fifty-fourth Research Institute of China Electronic Technology Group Corporation, Shijiazhuang  050081, China;
    3. Shaanxi Provincial Military Command Automation Station, Xi'an  710061, China)
  • Received:2013-04-14 Online:2015-02-20 Published:2015-04-14
  • Contact: JIANG Jianguo



关键词: 细菌觅食, 算法优化, 自适应学习, Tent映射, 高维函数优化, 局部搜索


An adaptive bacterial foraging optimization algorithm is presented due to the classic optimization algorithm's poor performance when optimizing high-dimensional complex functions. The fixed chemotactic step is improved as the adaptive sliding step which decreases nonlinearly with the result of strengthening the ability of local search. The adaptive dimension learning method for the optimal bacterium in the current cycle of chemotaxis is proposed so as to increase the accuracy of the solution and enhance the search efficiency. The elite bacterium is used as the initial point for Tent chaotic mapping to initialize the position of bacteria which meet the conditions of migration, and therefore the convergence speed of the algorithm is accelerated. Experimental result indicates that the algorithm outperforms the classic algorithm both in terms of  solution accuracy and convergence speed. And, the algorithm has a higher efficiency.

Key words: bacterial foraging, algorithm optimization, adaptive learning, tent map, high-dimensional function optimization, local search


  • TP301.6