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Eel and grouper optimizer: a nature-inspired optimization algorithm

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Abstract

This paper proposes a meta-heuristic called Eel and Grouper Optimizer (EGO). The EGO algorithm is inspired by the symbiotic interaction and foraging strategy of eels and groupers in marine ecosystems. The algorithm’s efficacy is demonstrated through rigorous evaluation using nineteen benchmark functions, showcasing its superior performance compared to established meta-heuristic algorithms. The findings and results on the benchmark functions demonstrate that the EGO algorithm outperforms well-known meta-heuristics. This work also considers solving a wide range of real-world practical engineering case studies including tension/compression spring, pressure vessel, piston lever, and car side impact, and the CEC 2020 Real-World Benchmark using EGO to illustrate the practicality of the proposed algorithm when dealing with the challenges of real search spaces with unknown global optima. The results show that the proposed EGO algorithm is a reliable soft computing technique for real-world optimization problems and can efficiently outperform the existing algorithms in the literature.

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The data produced during the current study can be obtained from the corresponding author upon reasonable request.

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Acknowledgements

The author would like to thank Mickey Charteris for providing his outstanding eel and grouper photos.

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No specific grant from any funding agency in the public, commercial, or not-for-profit sectors was received for this research.

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Authors and Affiliations

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

    Ali Mohammadzadeh

  2. MEU Research Unit, Middle East University, Amman, 11831, Jordan

    Ali Mohammadzadeh

  3. Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  4. University Research and Innovation Center, Obuda University, Budapest, Hungary

    Seyedali Mirjalili

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  1. Ali Mohammadzadeh
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The design and implementation of the research, the analysis of the results, and the writing of the manuscript were done by Ali Mohammadzadeh and Seyedali Mirjalili. All authors have read and approved the published version of the manuscript.

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Mohammadzadeh, A., Mirjalili, S. Eel and grouper optimizer: a nature-inspired optimization algorithm. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04545-w

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