Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (33): 29-33.

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Adaptive variable step size bacterial foraging optimization

CHEN Jianchao1, HU Guiwu1,2,3, DU Xiaoyong2,3   

  1. 1.School of Mathematics & Computational Science, Guangdong University of Business Studies, Guangzhou 510320, China
    2.Key Lab of Data Engineer and Knowledge Engineer for the Ministry of  Education, Beijing 100872, China
    3.School of Information, Renmin University of China, Beijing 100872, China
  • Online:2012-11-21 Published:2012-11-20

自适应变步长菌群优化算法

陈建超1,胡桂武1,2,3,杜小勇2,3   

  1. 1.广东商学院 数学与计算科学学院,广州 510320
    2.教育部数据工程与知识工程重点实验室,北京 100872
    3.中国人民大学 信息学院,北京 100872

Abstract: In view of the defects of weak exploring ability and so on caused by the same swim step in the bacterial foraging algorithm, clustering idea is introduced to compute and adjust the swim step adaptively, which reflects the collaborative and intelligent behavior among bacterial population and can improve algorithm’s performance, such as exploration and exploitation, local search and refining ability. In the comparison experiment between this paper’s algorithm and other 4 typical algorithms on 10 complex Benchmark functions, this paper’s algorithm has better search ability and efficiency than the others up to 60%~90% among the test functions, which shows this paper’s algorithm is a competent algorithm for solving numerical optimization problems.

Key words: bacterial foraging optimization, chemotactic step size, clustering, coordination

摘要: 针对菌群优化算法由于步长固定导致探索能力不强等缺陷,应用聚类思想自适应计算并调整细菌的趋化步长,体现了菌群之间的协同性和智能性行为,有效地提高算法的性能,比如探索能力和开发能力,特别是局部搜索和求精能力。在使用10个复杂的Benchmark函数所进行的对比实验中,所提出的算法在搜索能力和效率等方面优于其他典型算法的比率达到60%~90%,验证了改进算法是一种具有竞争力的优化算法。

关键词: 菌群优化算法, 趋化步长, 聚类, 协同性