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An enhanced whale optimization algorithm for clustering

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Abstract

Clustering is a technique of grouping the data objects into clusters. Many metaheuristic algorithms based on swarm intelligence, physic laws, and chemical reactions, among others, have been developed for clustering. In this study, an enhanced whale optimization algorithm (EWOA) is introduced to solve clustering problems. The whale optimization algorithm (WOA) is adapted and enhanced with two additional operational procedures. The position update equations from the water wave optimization algorithm are incorporated into the algorithm to improve the search space and accelerate the convergence rate. The tabu and neighbourhood search mechanisms were added to handle the local optima situation. The efficiency of the proposed EWOA is measured using a simulation-based experiment conducted on eight benchmark datasets, and the results obtained are then compared to seven existing clustering algorithms/techniques. The performance of each algorithm is compared and analyzed using the average intra-cluster distance and f-measure parameters. The experimental results demonstrated the applicability and feasibility of the enhancements that were made and proved the superiority of the proposed EWOA clustering algorithm.

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Acknowledgments

The authors would like to thank the Editors and the anonymous reviewers for their valuable comments and suggestions which has helped to improve the quality and clarity of the paper. The authors would also like to acknowledge the assistance rendered by Dr. Cherry Bhargava for the general supervision of the research group and general administrative support.

Data Availability

The data that support the findings of this study are available upon request from the corresponding authors.

Funding

This work was supported by the Ministry of Education, Malaysia under grant no. FRGS/1/2020/STG06/UCSI/02/1.

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Authors and Affiliations

  1. Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India

    Hakam Singh

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

    Vipin Rai & Neeraj Kumar

  3. Swami Keshvanand Institute of Technology, Management & Gramothan (SKIT), Jaipur, Rajasthan, 302017, India

    Pankaj Dadheech

  4. Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, MH, 412115, India

    Ketan Kotecha

  5. Faculty of Business and Management, UCSI University, Jalan Menara Gading, 56000 Cheras, Kuala Lumpur, Malaysia

    Ganeshsree Selvachandran

  6. Machine Intelligence Research Labs, Auburn, WA, 98071, USA

    Ajith Abraham

Authors
  1. Hakam Singh
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  2. Vipin Rai
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  3. Neeraj Kumar
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  4. Pankaj Dadheech
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  5. Ketan Kotecha
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  6. Ganeshsree Selvachandran
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  7. Ajith Abraham
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Contributions

All authors contributed to the conception and design of the study. Material preparation, data collection, data visualization and data analysis were performed by Hakam Singh, Vipin Rai, Neeraj Kumar, and Pankaj Dadheech. Advanced data analysis and validation were done by Ketan Kotecha, Ganeshsree Selvachandran and Ajith Abraham. The first draft of the manuscript was written by Hakam Singh, Vipin Rai, Neeraj Kumar, and Pankaj Dadheech. The second draft was prepared and edited by Ganeshsree Selvachandran and Ajith Abraham. All authors commented on previous versions of the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Ganeshsree Selvachandran.

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Singh, H., Rai, V., Kumar, N. et al. An enhanced whale optimization algorithm for clustering. Multimed Tools Appl 82, 4599–4618 (2023). https://doi.org/10.1007/s11042-022-13453-3

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  • DOI: https://doi.org/10.1007/s11042-022-13453-3

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