A novel heat transfer search algorithm and its application in solar cells

A swarm intelligent optimization strategy, namely, the elite opposition-based learning (EOBL) strategy, is proposed for the heat transfer search algorithm (HTSA), aiming to achieve global optimization solutions for non-linear optimization problems. An improved heat transfer search algorithm (IHTSA), the EOBL strategy, is proposed to enhance the correlation between the upper and lower generations. The performance of the IHTSA has been verified with nine test functions, and the results of the IHTSA are compared with the corresponding results of the instinctive reaction strategy based on Harris hawks optimization and the HTSA. The experimental results show that the IHTSA achieves the first rank in overall performance among the algorithms. Then, the IHTSA is applied to determine the parameters of photovoltaic models, i.e., the single diode model and double diode model. By comparing with the results in the literature, IHTSA results show that it is an effective optimization algorithm.