A review on meta-heuristics methods for estimating parameters of solar cells

Sun is considered as an important source of energy, and nowadays it is studied by researches from different areas. The current technologies are not able to convert solar energy into electricity with high performance. The tendency is to generate new methods that enhance the design of devices for solar energy conversion. Solar cells are devices that convert solar energy into electrical energy with low cost and easy large-scale manufacturing capabilities. However, such devices have a high degree of nonlinearity, and they possess parameters that must be accurately selected. Considering the above traditional computational methods are used to obtain solar cells parameters are cumbersome with many limitations. This paper presents a review of different meta-heuristics techniques, including Genetic Algorithms, Harmony Search, Artificial Bee Colony, Simulated Annealing, Cat Swarm Optimization, Differential Evolution, Particle Swarm Optimization, Advanced Bee Swarm Optimization, Whale Optimization Algorithm, Gravitational Search Algorithm, Flower Pollination Algorithm, Shuffled Complex Evolution, and Wind-Driven Optimization. Such methods are applied to solar cell parameters estimation which may be beneficial to enhance the efficiency of such devices. This study provides different comparisons to define which of them is the best alternative for solar cells design.