A hybrid snake and grey wolf optimizer for load frequency control in photovoltaic thermal power system
Journal of Supercomputing, cilt.82, sa.15, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 82 Sayı: 15
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11227-026-08872-0
- Dergi Adı: Journal of Supercomputing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Compendex, INSPEC, zbMATH, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Hybrid algorithm, Load frequency control, Optimization, Photovoltaic thermal power system
- Recep Tayyip Erdoğan Üniversitesi Adresli: Evet
Özet
Metaheuristic algorithms are widely employed to solve complex and high-dimensional real-world optimization problems. However, standalone algorithms often suffer from drawbacks such as premature convergence, stagnation in local optima, insufficient exploration of the search space, and unstable solution quality. To address these limitations, this study proposes a novel hybrid optimization algorithm, called SOGWO, by combining the leadership-guided search mechanism of the grey wolf optimizer (GWO) with the strategic update mechanisms of the snake optimizer (SO). The proposed hybrid structure aims to enhance exploration capability, maintain population diversity, and improve the balance between exploration and exploitation. Firstly, SOGWO is tested on 23 classical benchmark, 12 CEC-2022 test functions and compared with 13 well-known and recent algorithms to demonstrate its superiority. The experimental results demonstrate that SOGWO successfully combines the advantages of both constituent algorithms and consistently produces high-quality solutions. Statistical analyses indicate that SOGWO ranks second for the 10-dimensional problems and achieves the best overall performance for the 20-dimensional problems. To further assess its practical applicability, SOGWO is employed to optimize proportional–integral (PI) controller parameters for the load frequency control (LFC) problem in a two-area photovoltaic–thermal power system. The obtained results confirm the effectiveness of the proposed algorithm in solving a real-world engineering optimization problem and improving control performance. Moreover, the proposed SOGWO incurs a significant computational cost due to its population-based structure and applicability to large-scale problems, indicating that SOGWO has the potential to be suitable for high-performance computing (HPC), parallel, and distributed architectures. Consequently, the proposed algorithm can be suggested as a reliable and effective method for the solution of HPC and real-life engineering applications.