Simulated Annealing algorithm for photovoltaic parameters identification

By K. El-Naggar, M. Alrashidi, Mohamad F. AlHajri, A. K. Al-Othman. 2012

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Parameter estimation of solar cells and modules using an improved adaptive differential evolution algorithm (2013, methodology): “Generally, the parameter estimation methods can be categorized into analytical methods [11] and numerical methods [12–22].”
Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters (2018, methodology): “In the recent years, different optimization techniques have been introduced to estimate the parameters of solar PV, namely the genetic algorithm [8], pattern search optimization [9], artificial immune system [10], bacterial foraging algorithm [11], simulated annealing [12], harmony search algorithm ”
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].”