A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models.

ABSTRACT: This study conducts a comparative analysis of the performance of ten novel and well-performing metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimizatio

SUBMITTER: Zga A 

PROVIDER: S-EPMC11784897 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature