Parameter extraction of solar cells using particle swarm optimization

By Meiying Ye, Xiaodong Wang, Yousheng Xu. 2009

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Parameter identification of solar cells using artificial bee colony optimization (2014, methodology): “Such methods include GA (genetic algorithms) [17,18,24], PSO (particle swarm optimization) [12,19], SA (simulated annealing) [20], HS (harmony search) [13], BFA (Bacterial Foraging Algorithm) [21], TBLO (teaching-learning based optimization) [23] and BMO (bird matting optimization) [27].”
Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm (2013, methodology): “These parameter values were chosen as they are widely used in literatures [12,38].”
Parameter extraction of solar photovoltaic modules using penalty-based differential evolution (2012, methodology): “It has to be noted that the typical value of NP suggested by many researchers is 8D–10D [30].”
A New Parameter Estimation Method of Solar Photovoltaic (2018, methodology): “Additionally, in order to resolve the issues, as existing in numerical and analytical methods, several metaheuristic algorithms (MAs)-based approaches, such as, genetic algorithm [12], particle swarm algorithm [13], [14], simulated annealing (SA) algorithm [15], differential evolution algorithm [16]”
Parameter extraction of solar cell models using repaired adaptive differential evolution (2013, methodology): “For the single diode model, the extracted parameters and RMSE value of R cr -IJADE are compared with those of GA [26], CPSO [20], PS [7], SA [27], IGHS [28], and ABSO [1].”