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Recent advances in multi-objective grey wolf optimizer, its versions and applications

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Abstract

In this work, a comprehensive review of the multi-objective grey wolf optimizer (MOGWO) is provided. In multi-objective optimization (MO), more than one objective function must be considered at the same time. To deal with such problems, a priori or a posteriori MOGWO variants have been proposed in the literature. In the a priori model, the multi-objective functions are aggregated into a single objective function by a number of weights. In the posterior model, the multi-objective formulation is maintained and MOGWO is employed to estimate the Pareto optimal solutions representing the best trade-offs between the objectives. Due to the successful performance of MOGWO, it has been widely utilized for MO. This review covers the research growth of MOGWO in terms of a number of researches, topics, top researchers, etc. Furthermore, several versions of MOGWO have been introduced and reviewed with applications in diverse fields. This work also provides a critical analysis to show the shortcomings and limitations of using the basic version of MOGWO followed by several future directions. This review paper will be a base paper for any researcher interested to implement MOGWO in its work.

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Authors and Affiliations

  1. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE

    Sharif Naser Makhadmeh & Mohammed Azmi Al-Betar

  2. MLALP Research Group, University of Sharjah, Sharjah, UAE

    Osama Ahmad Alomari

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

    Seyedali Mirjalili

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

    Seyedali Mirjalili

  5. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, P.O. Box 50, Irbid, Jordan

    Mohammed Azmi Al-Betar

  6. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Ashraf Elnagar

Authors
  1. Sharif Naser Makhadmeh
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  2. Osama Ahmad Alomari
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  3. Seyedali Mirjalili
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  4. Mohammed Azmi Al-Betar
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  5. Ashraf Elnagar
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Corresponding author

Correspondence to Sharif Naser Makhadmeh.

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Makhadmeh, S.N., Alomari, O.A., Mirjalili, S. et al. Recent advances in multi-objective grey wolf optimizer, its versions and applications. Neural Comput & Applic 34, 19723–19749 (2022). https://doi.org/10.1007/s00521-022-07704-5

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