An ant colony approach for clustering
An ant colony approach for clustering
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DOI：
10.1016/j.aca.2003.12.032
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发表时间：
2004-05-03
影响因子：
6.2
通讯作者：
Kulkarni, BD
中科院分区：
文献类型：
--
作者：
Shelokar, PS;Jayaraman, VK;Kulkarni, BD
本文提出了一种蚁群优化方法，最优聚类N个对象到K个集群。该算法采用分布式代理，模仿真实的蚂蚁找到从巢穴到食物源和返回的最短路径的方式。该算法已在几个模拟和真实的数据集上实现和测试。该算法的性能进行了比较与其他流行的随机/启发式方法，即遗传算法，模拟退火和禁忌搜索。我们的计算模拟揭示了非常令人鼓舞的结果，在质量的解决方案，平均数量的功能评估和所需的处理时间。(C)2003 Elsevier B. V.保留所有权利。
This paper presents an ant colony optimization methodology for optimally clustering N objects into K clusters. The algorithm employs distributed agents which mimic the way real ants find a shortest path from their nest to food source and back. This algorithm has been implemented and tested on several simulated and real datasets. The performance of this algorithm is compared with other popular stochastic/heuristic methods viz. genetic algorithm, simulated annealing and tabu search. Our computational simulations reveal very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required. (C) 2003 Elsevier B.V. All rights reserved.