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.
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]
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Database:	Engineering Source

Description
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]
ISSN:	19929978