Clustering using firefly algorithm: Performance study
Clustering using firefly algorithm: Performance study
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DOI：
10.1016/j.swevo.2011.06.003
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发表时间：
2011-09-01
影响因子：
10
通讯作者：
Mani, V.
中科院分区：
文献类型：
--
作者：
Senthilnath, J.;Omkar, S. N.;Mani, V.
萤火虫算法(FA)是最近提出的一种受自然启发的优化算法，模拟了萤火虫的闪光模式和特征。聚类是一种流行的数据分析技术，用于根据对象的属性值识别同质对象组。本文将该算法用于基准问题的聚类，并与其他两种自然启发技术--人工蜂群算法(ABC)、粒子群算法(PSO)和文献中使用的其他九种方法进行了比较。使用来自UCI机器学习储存库的13个典型基准数据集来演示该技术的结果。从得到的结果，我们比较了FA算法的性能，得出FA可以有效地用于聚类的结论。皇冠版权所有(C)2011由爱思唯尔有限公司出版。保留所有权利。
A Firefly Algorithm (FA) is a recent nature inspired optimization algorithm, that simulates the flash pattern and characteristics of fireflies. Clustering is a popular data analysis technique to identify homogeneous groups of objects based on the values of their attributes. In this paper, the FA is used for clustering on benchmark problems and the performance of the FA is compared with other two nature inspired techniques - Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and other nine methods used in the literature. Thirteen typical benchmark data sets from the UCI machine learning repository are used to demonstrate the results of the techniques. From the results obtained, we compare the performance of the FA algorithm and conclude that the FA can be efficiently used for clustering. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.