A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

Q3 Computer Science
Y. Kumar, G. Sahoo
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引用次数: 78

Abstract

Natural phenomenon's and swarms behavior are the warm area of research among the researchers. A large number of algorithms have been developed on the account of natural phenomenon's and swarms behavior. These algorithms have been implemented on the various computational problems for the sake of solutions and provided significant results than conventional methods but there is no such algorithm which can be applied for all of the computational problems. In 2009, a new algorithm was developed on the behalf of theory of gravity and was named gravitational search algorithm (GSA) for continuous optimization problems. In short span of time, GSA algorithm gain popularity among researchers and has been applied to large number of problems such as clustering, classification, parameter identification etc. This paper presents the compendious survey on the GSA algorithm and its applications as well as enlightens the applicability of GSA in data clustering & classification.
引力搜索算法及其在数据聚类分类中的应用综述
自然现象和群体行为是研究者们研究的热点。大量的算法是基于自然现象和群体行为而发展起来的。这些算法已经在各种计算问题上实现,并提供了比传统方法显著的结果,但没有这样的算法可以适用于所有的计算问题。2009年,针对连续优化问题,提出了一种代表引力理论的新算法,命名为引力搜索算法(gravity search algorithm, GSA)。在很短的时间内,GSA算法受到了研究人员的欢迎,并被应用于大量的聚类、分类、参数识别等问题。本文简要介绍了GSA算法及其应用,并对GSA在数据聚类和分类中的适用性进行了阐述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Intelligent Systems and Applications in Engineering
International Journal of Intelligent Systems and Applications in Engineering Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.30
自引率
0.00%
发文量
18
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