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A new NEST-IGWO strategy for determining optimal IGWO control parameters

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Abstract

Online optimization applications require fast convergence without sacrificing accuracy. Although the gray wolf optimization (GWO) algorithm is showing good convergence performance, it still needs further improvement to achieve these requirements. Optimal determination of the GWO control parameters can substantially improve the converge performance. All studies in the literature introduced efforts in tuning these parameters on try-and-error bases which may not satisfy the requirements of the online applications. For this reason, a novel nested improved GWO (NEST-IGWO) is used to determine the optimal control parameters for the IGWO. This novel strategy substantially improved the convergence time and accuracy, especially with online control systems. This strategy is having two nested IGWO loops. The internal IGWO loop includes the target function needed to be optimized. Meanwhile, the external loop is used to optimally determine the control parameters of the internal one. The objective function of the external loop is the failure rate and convergence time of the internal one. The results obtained from the NEST-IGWO are compared to 10 existing optimization algorithms for 10 different benchmark functions. Moreover, these optimization algorithms were applied to determine the parameters of the PV-cell model as a real-world application. The results showed that NEST-IGWO outperformed the other 10 optimization algorithms for all benchmark functions understudy and the estimations of the PV-cell parameters in terms of failure rate and convergence time. With the use of the NEST-IGWO, the convergence time is reduced by 90% of the average convergence time for all other algorithms. Moreover, the failure rate is reduced to 0% which is not the case for other algorithms understudy. These outstanding results prove the superiority of the NEST-IGWO compared to the other algorithms, and it opens a new venue for determining optimal control parameters for all optimization algorithms.

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Funding

This work was supported by the King Saud University, Saudi Arabia, Deanship of Scientific research, Research Chair Saudi Electricity Company Chair in Power System Reliability and Security.

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Authors and Affiliations

  1. Computers and Control Department Faculty of Engineering Mansoura University, Mansoura, Egypt

    Asmaa H. Rabie

  2. Electrical Engineering Department, Mansoura University, Mansoura, Egypt

    Ali M. Eltamaly

  3. Sustainable Energy Technologies Center, King Saud University, Riyadh, 11421, Saudi Arabia

    Ali M. Eltamaly

  4. Saudi Electricity Company Chair in Power System Reliability and Security, King Saud University, Riyadh, 11421, Saudi Arabia

    Ali M. Eltamaly

Authors
  1. Asmaa H. Rabie
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  2. Ali M. Eltamaly
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Contributions

All authors contributed to the study's conception and design. Material preparation, data collection and analysis were performed by Asmaa H. Rabie and Ali M. Eltamaly. The first draft of the manuscript was written by Asmaa H. Rabie and Ali M. Eltamaly and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ali M. Eltamaly.

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Rabie, A.H., Eltamaly, A.M. A new NEST-IGWO strategy for determining optimal IGWO control parameters. Neural Comput & Applic 35, 15143–15165 (2023). https://doi.org/10.1007/s00521-023-08535-8

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  • DOI: https://doi.org/10.1007/s00521-023-08535-8

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