[1]汪 洋,谢 芬,刘 清.基于DBSCAN聚类的细菌自适应步长觅食算法[J].南京师范大学学报(工程技术版),2014,14(03):063.
　Wang Yang,Xie Fen,Liu Qing.DBSCAN-Based Adaptive Bacterial Foraging Algorithm[J].Journal of Nanjing Normal University(Engineering and Technology),2014,14(03):063.
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基于DBSCAN聚类的细菌自适应步长觅食算法
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
14卷
期数:
2014年03期
页码:
063
栏目:
出版日期:
2014-09-30

文章信息/Info

Title:
DBSCAN-Based Adaptive Bacterial Foraging Algorithm
作者:
汪 洋; 谢 芬; 刘 清
南京师范大学计算机科学与技术学院,江苏 南京 210023
Author(s):
Wang Yang; Xie Fen; Liu Qing
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
关键词:
细菌觅食算法; 自适应步长; 算法早熟; DBSAN
Keywords:
BFA; adaptive chemotactic step size; algorithm premature; DBSAN
分类号:
TP18
文献标志码:
A
摘要:
自适应细菌觅食算法(adaptive bacterial foraging algorithm,ABFA)在一定程度上解决了经典觅食算法步长选择的问题,加快了算法的收敛速度.但随着细菌代价函数值的减小,自适应细菌觅食算法原有的趋化步长调整函数易使步长快速进入极小,造成算法早熟.本文提出了一种基于DBSCAN聚类的细菌自适应步长觅食算法(DBSCAN-based adaptive bacterial foraging algorithm,DBSCAN-ABFA),算法利用DBSCAN聚类对核心点区域的细菌进行标记,通过对被标记细菌采用改进的趋化步长调整函数,降低自适应步长的缩小速率来解决步长快速进入极小的问题,最终避免算法早熟,并通过实验验证了算法的有效性.
Abstract:
The adaptive bacterial foraging algorithm(ABFA),to some extent,solves the problem of chemotactic step size choice in bacterial foraging algorithm(BFA)and subsequently accelerates the convergence rate.However,along with the decrease of bacterial cost function value,the original chemotactic step size adjust function is liable to make chemotactic step size minimum,leading to the algorithm premature.Adaptive bacterial foraging algorithm based on density-based spatial clustering of applications with noise(DBSCAN-based adaptive bacterial foraging algorithm,DBSCAN-ABFA)is designed,with the purpose of avoiding the algorithm premature by changing the chemotactic step size adjust function of the labeled core points bacterial according to DBSCAN,and the improved chemotactic step size adjust function can reduce the shrink rate of step size,so the algorithm premature is ultimately avoided.To verify the feasibility of the algorithm,trials are also designed.

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备注/Memo

备注/Memo:
收稿日期:2014-05-20.
基金项目:国家自然科学基金(61103185)、江苏省高校自然科学
基金项目(10KJD520004).
通讯联系人:汪洋,硕士研究生,研究方向:人工智能、图像处理.E-mail:leo_tony163@163.com
更新日期/Last Update: 2014-09-30