检索结果-内蒙古大学图书馆

A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems

引用

Iran Journal of Computer Science 2024年第1期7卷 65-101页

作者： Haeri Boroujeni, Sayed Pedram Pashaei, Elnaz School of Computing Clemson University Clemson 29634 SC United States Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis United States

This paper focuses on connectivity-based data clustering for categorizing similar and dissimilar data into distinct groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization algorithm (ChOA), generalized normal distribution algorithm (GNDA), and Opposition-Based Learning (OBL) method. First, two versions of ChOA with two different dynamic coefficients and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Second, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. In this study, eight benchmark datasets, including five datasets of the UCI machine learning repository and three challenging shape datasets, are used to investigate the general performance of the proposed method. The results are evaluated against several popular and recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the Sum of Intra-Cluster Distances (SICD), Error Rate (ER), and convergence rate. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Chimp optimization algorithm Data clustering generalized normal distribution algorithm K-means Meta-heuristic optimization algorithm Opposition-based learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：