An Improved Grey Wolf Optimizer with Adaptive Multi-operator Search for Numerical Optimization and Dynamic Photovoltaic MPPT.
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| Title: | An Improved Grey Wolf Optimizer with Adaptive Multi-operator Search for Numerical Optimization and Dynamic Photovoltaic MPPT. |
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| Authors: | Liu, Lei1 liulei@yku.edu.cn, Li, Zhongfeng2 afeng0601@163.com, Lu, Yijia3 13214152628@163.com, Hao, Shuang4 haoshuang@yku.edu.cn, Jin, Bin2 jinbin@yku.edu.cn, Zhao, Zhenlong1 zhaozhenlong@yku.edu.cn |
| Source: | IAENG International Journal of Applied Mathematics. Jul2026, Vol. 56 Issue 7, p2888-2903. 16p. |
| Subjects: | Grey Wolf Optimizer algorithm, Maximum power point trackers, Mathematical optimization, Renewable energy sources, Metaheuristic algorithms, Engineering design, Dynamical systems |
| Abstract: | This paper proposes an improved Grey Wolf Optimizer (IGWO) for complex optimisation problems and investigates its application to photovoltaic MPPT under dynamic operating conditions. The proposed algorithm enhances the original GWO by introducing a fitness-based leadership weighting strategy, a hybrid multi-operator search mechanism, and progression-aware parameter control. A reward-driven adaptive operator selection scheme is further developed to adjust the contribution of different search operators according to their recent optimisation performance, thereby improving search efficiency and robustness. The optimisation capability of IGWO is first evaluated on the CEC2017 benchmark suite. Statistical results based on the Friedman and Wilcoxon tests show that IGWO consistently achieves superior performance compared with several state-of-the-art metaheuristic algorithms. The algorithm is further validated on nine constrained engineering design problems, where IGWO obtains the best overall ranking with the lowest total rank. Finally, the proposed method is applied to MPPT control in photovoltaic systems under time-varying irradiance conditions. Simulation results indicate that the IGWO-based MPPT strategy can rapidly track the global maximum power point while maintaining stable converter operation with reduced steady-state oscillation. These results demonstrate that IGWO provides an effective optimisation framework for both engineering design problems and renewable energy control applications. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195026920 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Improved Grey Wolf Optimizer with Adaptive Multi-operator Search for Numerical Optimization and Dynamic Photovoltaic MPPT. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Lei%22">Liu, Lei</searchLink><relatesTo>1</relatesTo><i> liulei@yku.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhongfeng%22">Li, Zhongfeng</searchLink><relatesTo>2</relatesTo><i> afeng0601@163.com</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Yijia%22">Lu, Yijia</searchLink><relatesTo>3</relatesTo><i> 13214152628@163.com</i><br /><searchLink fieldCode="AR" term="%22Hao%2C+Shuang%22">Hao, Shuang</searchLink><relatesTo>4</relatesTo><i> haoshuang@yku.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Jin%2C+Bin%22">Jin, Bin</searchLink><relatesTo>2</relatesTo><i> jinbin@yku.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Zhenlong%22">Zhao, Zhenlong</searchLink><relatesTo>1</relatesTo><i> zhaozhenlong@yku.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Jul2026, Vol. 56 Issue 7, p2888-2903. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+power+point+trackers%22">Maximum power point trackers</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+design%22">Engineering design</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper proposes an improved Grey Wolf Optimizer (IGWO) for complex optimisation problems and investigates its application to photovoltaic MPPT under dynamic operating conditions. The proposed algorithm enhances the original GWO by introducing a fitness-based leadership weighting strategy, a hybrid multi-operator search mechanism, and progression-aware parameter control. A reward-driven adaptive operator selection scheme is further developed to adjust the contribution of different search operators according to their recent optimisation performance, thereby improving search efficiency and robustness. The optimisation capability of IGWO is first evaluated on the CEC2017 benchmark suite. Statistical results based on the Friedman and Wilcoxon tests show that IGWO consistently achieves superior performance compared with several state-of-the-art metaheuristic algorithms. The algorithm is further validated on nine constrained engineering design problems, where IGWO obtains the best overall ranking with the lowest total rank. Finally, the proposed method is applied to MPPT control in photovoltaic systems under time-varying irradiance conditions. Simulation results indicate that the IGWO-based MPPT strategy can rapidly track the global maximum power point while maintaining stable converter operation with reduced steady-state oscillation. These results demonstrate that IGWO provides an effective optimisation framework for both engineering design problems and renewable energy control applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 2888 Subjects: – SubjectFull: Grey Wolf Optimizer algorithm Type: general – SubjectFull: Maximum power point trackers Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Engineering design Type: general – SubjectFull: Dynamical systems Type: general Titles: – TitleFull: An Improved Grey Wolf Optimizer with Adaptive Multi-operator Search for Numerical Optimization and Dynamic Photovoltaic MPPT. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Lei – PersonEntity: Name: NameFull: Li, Zhongfeng – PersonEntity: Name: NameFull: Lu, Yijia – PersonEntity: Name: NameFull: Hao, Shuang – PersonEntity: Name: NameFull: Jin, Bin – PersonEntity: Name: NameFull: Zhao, Zhenlong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19929978 Numbering: – Type: volume Value: 56 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Applied Mathematics Type: main |
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