Belmadani, HamzaMerabet, OussamaObelaid, AdelKheldoun, AissaMohit, BajajAnsari, Md FahimBradai, Rafik2024-04-232024-04-232023979-835035874-2https://ieeexplore.ieee.org/document/1039005310.1109/AESPC59761.2023.10390053https://dspace.univ-boumerdes.dz/handle/123456789/13844Based on the Seagull Optimization approach, this paper proposes a completely new, rapid Maximum Power Point tracking method. After adding opposition learning and adjusting the convergence factor to the initial version, the intended algorithm - dubbed The Guided Seagull Optimizer (GSO) - was produced. Essentially, the goal of the new technique is to increase convergence speed while maintaining a reasonable global search capability. The GSO algorithm was tested on a stand-alone photovoltaic system subjected to complex multi-peak partial shadowing patterns. Overall, the findings reveal that the technique outperforms typical SOA and PSO algorithms when it comes to of convergence time, efficiency, and adaptability.enMaximum Power Point TrackingMetaheuristic algorithmsPartial ShadingPhotovoltaic systemsSeagull Optimization AlgorithmArticle