Boosted mutation-based Harris hawks optimizer for parameters identification of single-diode solar cell models

In order to realize the performance of the PV model before being installed, it is often indispensable to develop reliable and accurate parameter identification methods for dealing with the PV models. Up to now, several stochastic methods have been proposed to analyze the feature space of this problem. However, some of the stochastic-based methods may present unsatisfactory results due to their insufficient exploration and exploitation inclinations, and the multimodal and nonlinearity existed in PV parameters extraction problems. In this paper, a Boosted Harris Hawk's Optimization (BHHO) technique is proposed to achieve a more stable model and effectively estimate the parameters of the single diode PV model. The BHHO method combines random exploratory steps of evolution inspired by the flower pollination algorithm (FPA) and a powerful mutation scheme of the differential evolution (DE) with 2-Opt algorithms. The proposed strategies not only help BHHO algorithm to accelerate the convergence rate but also assist it in scanning new regions of the search basins. The results demonstrate that the proposed BHHO is more accurate and reliable compared to the basic version and several well-established methods. The BHHO method was rigorously validated by using real experimental data under seven sunlight and temperature conditions. Furthermore, the statistical criteria indicate that the proposed BHHO method has lower errors among other peers, which is highly useful for real-world applications.