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中国科学院软件研究所
  
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徐晓华,陈崚.一种自适应的蚂蚁聚类算法.软件学报,2006,17(9):1884-1889
一种自适应的蚂蚁聚类算法
An Adaptive Ant Clustering Algorithm
投稿时间:2004-06-13  修订日期:2005-08-29
DOI:
中文关键词:  群体智能  蚁群聚类
英文关键词:swarm intelligence  ant clustering
基金项目:Supported by the National Natural Science Foundation of China under Grant No.60074013 (国家自然科学基金); the Chinese National Foundation for Science and Technology Development under Grant No.2003BA614A-14 (国家科技攻关项目); the Natural Science Foundation of Jiangsu Province of China under Grant No.BK2005047 (江苏省自然科学基金); the Open Foundation of State Key Laboratory for Novel Software Technology (Nanjing University) of China (计算机软件新技术国家重点实验室(南京大学)开放基金)
作者单位
徐晓华 南京航空航天大学,信息科学与技术学院,江苏,南京,210016 
陈崚 扬州大学,计算机科学与工程系,江苏,扬州,225009
计算机软件新技术国家重点实验室,南京大学,江苏,南京,210093 
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中文摘要:
      受蚂蚁分巢居住行为的启发,提出一种人工蚂蚁运动(ant movement,简称AM)模型和在此模型上的一个自适应的蚂蚁聚类算法(adaptive ant clustering,简称AAC).将人工蚂蚁看成一个行为简单的Agent,代表一个数据对象.在AM中,人工蚂蚁有睡眠和活跃两种状态.在AAC算法中,定义了一个适应度函数用来衡量蚂蚁与其邻居的相似程度.人工蚂蚁通过其适应度和激活概率函数来决定处于活跃态或者睡眠态.整个蚂蚁群体在移动中动态地、自适应地、自组织地形成多个独立的子群体,使不同类别的蚂蚁之间相互
英文摘要:
      Enlightened by the behaviors of gregarious ant colonies, an artificial ant movement (AM) model and an adaptive ant clustering (AAC) algorithm for this model are presented. In the algorithm, each ant is treated as an agent to represent a data object. In the AM model, each ant has two states: sleeping state and active state. In the algorithm AAC, the ant’s state is controlled by both a function of the ant’s fitness to the environment it locates and a probability function for the ants becoming active. By moving dynamically, the ants form different subgroups adaptively, and consequently the whole ant group dynamically self-organizes into distinctive and independent subgroups within which highly similar ants are closely connected. The result of data objects clustering is therefore achieved. This paper also present a method to adaptively update the parameters and the ants’ local movement strategies which greatly improve the speed and the quality of clustering. Experimental results show that the AAC algorithm on the AM model is much superior to other ant clustering methods such as BM and LF in terms of computational cost, speed and quality. It is adaptive, robust and efficient, and achieves high autonomy, simplicity and efficiency. It is suitable for solving high dimensional and complicated clustering problems.
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