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A metaheuristic approach based on coronavirus herd immunity optimiser for breast cancer diagnosis

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Abstract

As one of the important concepts in epidemiology, herd immunity was recommended to control the COVID-19 pandemic. Inspired by this technique, the Coronavirus Herd Immunity Optimiser has recently been introduced, demonstrating promising results in addressing optimisation problems. This particular algorithm has been utilised to address optimisation problems widely; However, there is room for enhancement in its performance by making modifications to its parameters. This paper aims to improve the Coronavirus Herd Immunity Optimisation algorithm to employ it in addressing breast cancer diagnosis problem through feature selection. For this purpose, the algorithm was discretised after the improvements were made. The Opposition-Based Learning approach was applied to balance the exploration and exploitation stages to enhance performance. The resulting algorithm was employed in the diagnosis of breast cancer, and its performance was evaluated on ten benchmark functions. According to the simulation results, it demonstrates superior performance in comparison with other well-known approaches of the similar nature. The results demonstrate that the new approach performs well in diagnosing breast cancer with high accuracy and less computational complexity and can address a variety of real-world optimisation problems.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Funding

The authors declare that no funds, grants, or other supports were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Engineering, Heris Branch, Islamic Azad University, Heris, Iran

    Ali Hosseinalipour

  2. Faculty of Science and Engineering, Southern Cross University, Gold Coast, Australia

    Reza Ghanbarzadeh

  3. Department of Software Engineering, Faculty of Engineering and Natural Science, Istinye University, Istanbul, Turkey

    Bahman Arasteh

  4. Applied Science Research Center, Applied Science Private University, Amman, Jordan

    Bahman Arasteh

  5. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Farhad Soleimanian Gharehchopogh

  6. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia

    Seyedali Mirjalili

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  1. Ali Hosseinalipour
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  4. Farhad Soleimanian Gharehchopogh
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Contributions

Ali Hosseinalipour: conceptualization, methodology, data curation, writing—original draft, reviewing and editing. Reza Ghanbarzadeh: conceptualization, methodology, writing—original draft, reviewing and editing. Bahman Arasteh: software, data curation, reviewing and editing. Farhad Soleimanian Gharehchopogh: validation, writing, reviewing and editing. Seyedali Mirjalili: conceptualization, validation, reviewing and editing.

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Correspondence to Ali Hosseinalipour.

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Hosseinalipour, A., Ghanbarzadeh, R., Arasteh, B. et al. A metaheuristic approach based on coronavirus herd immunity optimiser for breast cancer diagnosis. Cluster Comput 27, 9451–9475 (2024). https://doi.org/10.1007/s10586-024-04360-3

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