Multi-strategy Enhanced Grey Wolf Optimizer for Numerical Optimization and Its Application to Feature Selection

Wenmin Xu; Wen Long

doi:10.62762/JMIA.2025.522565

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Journal of Mathematics and Interdisciplinary Applications, Volume 1, Issue 1, 2025: 51-71

Open Access | Research Article | 29 November 2025

Multi-strategy Enhanced Grey Wolf Optimizer for Numerical Optimization and Its Application to Feature Selection

Wenmin Xu 1

Wen Long 1 *

1 School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China

* Corresponding Author: Wen Long, [email protected]

DOI: 10.62762/JMIA.2025.522565

Received: 02 September 2025, Accepted: 28 October 2025, Published: 29 November 2025

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Abstract

Grey wolf optimizer (GWO) is an effective meta-heuristic technique which has been widely utilized to solve numerical optimization as well as real-world applications. However, GWO has some shortcomings, i.e., low solution accuracy, slow convergence, and easy stagnation at local optima in solving complex problems. To tackle these shortcomings, an enhanced GWO called EGWO is developed in this study. This enhancement is achieved by embedding three novel strategies into the basic GWO to improve its performance. Firstly, a new transition mechanism is designed instead of the original strategy to obtain a good transition from the exploration to exploitation. Secondly, the cuckoo search algorithm is introduced for the decision layer individuals ($\alpha$, $\beta$, and $\delta$) to further improve the local search capability. Thirdly, an adaptive position search equation is proposed by using a dynamical parameter to generate new potential candidate position. The effectiveness of EGWO is verified on 25 classical benchmarks, three engineering problems, and 16 feature selection problems. The results show that EGWO performs better than the original GWO and other meta-heuristic methods in terms of solution accuracy and convergence speed.

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Keywords

grey wolf optimizer

cuckoo search

numerical optimization

Engineering optimization

feature selection

Data Availability Statement

Data will be made available on request.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 12361106; in part by the Guizhou Provincial Science and Technology Plan Key Projects of Qiankehe Jichu under Grant ZK[2023]003; in part by the Guizhou Provincial High Level Innovative Talent Training Plan Project of Qiankehe Platform Talent under Grant GCC[2023]006.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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Xu, W., & Long, W. (2025). Multi-strategy Enhanced Grey Wolf Optimizer for Numerical Optimization and Its Application to Feature Selection. Journal of Mathematics and Interdisciplinary Applications, 1(1), 51–71. https://doi.org/10.62762/JMIA.2025.522565

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TY  - JOUR
AU  - Xu, Wenmin
AU  - Long, Wen
PY  - 2025
DA  - 2025/11/29
TI  - Multi-strategy Enhanced Grey Wolf Optimizer for Numerical Optimization and Its Application to Feature Selection
JO  - Journal of Mathematics and Interdisciplinary Applications
T2  - Journal of Mathematics and Interdisciplinary Applications
JF  - Journal of Mathematics and Interdisciplinary Applications
VL  - 1
IS  - 1
SP  - 51
EP  - 71
DO  - 10.62762/JMIA.2025.522565
UR  - https://www.icck.org/article/abs/JMIA.2025.522565
KW  - grey wolf optimizer
KW  - cuckoo search
KW  - numerical optimization
KW  - Engineering optimization
KW  - feature selection
AB  - Grey wolf optimizer (GWO) is an effective meta-heuristic technique which has been widely utilized to solve numerical optimization as well as real-world applications. However, GWO has some shortcomings, i.e., low solution accuracy, slow convergence, and easy stagnation at local optima in solving complex problems. To tackle these shortcomings, an enhanced GWO called EGWO is developed in this study. This enhancement is achieved by embedding three novel strategies into the basic GWO to improve its performance. Firstly, a new transition mechanism is designed instead of the original strategy to obtain a good transition from the exploration to exploitation. Secondly, the cuckoo search algorithm is introduced for the decision layer individuals ($\alpha$, $\beta$, and $\delta$) to further improve the local search capability. Thirdly, an adaptive position search equation is proposed by using a dynamical parameter to generate new potential candidate position. The effectiveness of EGWO is verified on 25 classical benchmarks, three engineering problems, and 16 feature selection problems. The results show that EGWO performs better than the original GWO and other meta-heuristic methods in terms of solution accuracy and convergence speed.
SN  - pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Xu2025Multistrat,
  author = {Wenmin Xu and Wen Long},
  title = {Multi-strategy Enhanced Grey Wolf Optimizer for Numerical Optimization and Its Application to Feature Selection},
  journal = {Journal of Mathematics and Interdisciplinary Applications},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {51-71},
  doi = {10.62762/JMIA.2025.522565},
  url = {https://www.icck.org/article/abs/JMIA.2025.522565},
  abstract = {Grey wolf optimizer (GWO) is an effective meta-heuristic technique which has been widely utilized to solve numerical optimization as well as real-world applications. However, GWO has some shortcomings, i.e., low solution accuracy, slow convergence, and easy stagnation at local optima in solving complex problems. To tackle these shortcomings, an enhanced GWO called EGWO is developed in this study. This enhancement is achieved by embedding three novel strategies into the basic GWO to improve its performance. Firstly, a new transition mechanism is designed instead of the original strategy to obtain a good transition from the exploration to exploitation. Secondly, the cuckoo search algorithm is introduced for the decision layer individuals (\$\alpha\$, \$\beta\$, and \$\delta\$) to further improve the local search capability. Thirdly, an adaptive position search equation is proposed by using a dynamical parameter to generate new potential candidate position. The effectiveness of EGWO is verified on 25 classical benchmarks, three engineering problems, and 16 feature selection problems. The results show that EGWO performs better than the original GWO and other meta-heuristic methods in terms of solution accuracy and convergence speed.},
  keywords = {grey wolf optimizer, cuckoo search, numerical optimization, Engineering optimization, feature selection},
  issn = {pending},
  publisher = {Institute of Central Computation and Knowledge}
}

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