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Adaptive grey wolf optimizer

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Abstract

Swarm-based metaheuristic optimization algorithms have demonstrated outstanding performance on a wide range of optimization problems in both science and industry. Despite their merits, a major limitation of such techniques originates from non-automated parameter tuning and lack of systematic stopping criteria that typically leads to inefficient use of computational resources. In this work, we propose an improved version of grey wolf optimizer (GWO) named adaptive GWO which addresses these issues by adaptive tuning of the exploration/exploitation parameters based on the fitness history of the candidate solutions during the optimization. By controlling the stopping criteria based on the significance of fitness improvement in the optimization, AGWO can automatically converge to a sufficiently good optimum in the shortest time. Moreover, we propose an extended adaptive GWO (\(\hbox {AGWO}^\varDelta\)) that adjusts the convergence parameters based on a three-point fitness history. In a thorough comparative study, we show that AGWO is a more efficient optimization algorithm than GWO by decreasing the number of iterations required for reaching statistically the same solutions as GWO and outperforming a number of existing GWO variants.

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Funding

This work is supported by the start-up fund provided by CMU Mechanical Engineering, USA, and funding from National Science Foundation (CBET–1953222), United States.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Kazem Meidani, AmirPouya Hemmasian & Amir Barati Farimani

  2. Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Adelaide, Australia

    Seyedali Mirjalili

  3. Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea

    Seyedali Mirjalili

  4. Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA

    Amir Barati Farimani

  5. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA

    Amir Barati Farimani

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  1. Kazem Meidani
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  2. AmirPouya Hemmasian
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  4. Amir Barati Farimani
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Correspondence to Amir Barati Farimani.

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The code of the algorithm can be accessed from: https://github.com/BaratiLab/Adaptive-Grey-Wolf-Optimization-Algorithm-AGWO.

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Meidani, K., Hemmasian, A., Mirjalili, S. et al. Adaptive grey wolf optimizer. Neural Comput & Applic 34, 7711–7731 (2022). https://doi.org/10.1007/s00521-021-06885-9

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