Delayed dynamic step shuffling frog-leaping algorithm for optimal design of photovoltaic models

Developing an accurate mathematical model is an essential tool for studying and optimizing the performance of the photovoltaic cell system (PV). Transforming the PV problem into an optimization problem in which meta-heuristic algorithms excel provides an alternative approach to identifying the PV model’s parameters. The memetic evolution mechanism and the shuffling strategy included in the shuffling frog-leaping algorithm (SFLA) provide safeguards for solving nonlinear, multimodal, and high-dimensional problems. However, the low convergence accuracy is an essential drawback of the SFLA algorithm to solve the PV problem. This paper proposes a delayed dynamic step mechanism based on the SFLA algorithm’s characteristics to overcome this disadvantage, called the DDSFLA algorithm. The results of testing 23 benchmark functions and extracting the single diode model, the double-diode model, and the PV module show that the DDSFLA algorithm has a faster convergence speed and higher convergence accuracy and exhibits strong optimization stability under the special conditions of different temperatures or light intensities. The results suggest that the proposed algorithm can be used as an effective method to handle PV models’ parameter extraction. The extra resources and online user guidance for this research will be provided at https://aliasgharheidari.com. •Proposing delayed dynamic step mechanism for shuffling frog-leaping algorithm.•The variant SFLA algorithm performs better than some swarm-intelligence algorithms.•The variant SFLA algorithm exhibits strong robustness to PV models under different conditions.