Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Mandakini Behera, Archana Sarangi, Debahuti Mishra, Pradeep Kumar Mallick, Jana Shafi, Parvathaneni Naga Srinivasu and Muhammad Fazal Ijaz ()
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Mandakini Behera: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India
Archana Sarangi: Department of Electronics and Communication Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India
Debahuti Mishra: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India
Pradeep Kumar Mallick: School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
Jana Shafi: Department of Computer Science, College of Arts and Science, Prince Sattam bin Abdul Aziz University, Wadi Ad-Dawasir 11991, Saudi Arabia
Parvathaneni Naga Srinivasu: Department of Computer Science and Engineering, Prasad V. Potluri Siddhartha Institute of Technology, Vijayawada 520007, Andhra Pradesh, India
Muhammad Fazal Ijaz: Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea

Mathematics, 2022, vol. 10, issue 19, 1-29

Abstract: Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optimization algorithm utilizing ten UCI Machine Learning Repository datasets and eight Shape sets for performance evaluation. In addition to this, two clustering validity measures, Compact-separated and David–Bouldin, have been used for analyzing the efficiency of these algorithms. The experimental results show that the two proposed hybrid algorithms outperform the existing hybrid firefly particle swarm optimization algorithm.

Keywords: hybrid firefly particle swarm optimization algorithm; crazy firefly algorithm; variable step size firefly algorithm; compact-separated validity index; David–Bouldin validity index (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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