This work introduces a metaheuristic (MH) optimization method, which is inspired by the red-tailed hawks' predatory behavior is Improved Red-tailed Hawk (IRTHA) Algorithm. The algorithm uses a dynamic adjustment method which uses the combined effect of nonlinear decay and chaotic mapping to enhance the convergence efficacy and accuracy of outcomes. This enhancement affects the search radius of the algorithm and creates diversity in the dive speed of hawks, hence adaptively balancing exploration and exploitation, enhancing diversity and convergence. IRTHA's efficacy is examined for single, double, and triple diode models of various photovoltaic (PV) cells and modules, such as RTC France, PVM 752, STP 120/36, STM 40/36, and Photowatt-PWP201. A comparative analysis of IRTHA with other advanced MH optimization techniques indicates that IRTHA exhibits considerably lower RMSE values: 7.72986E-04 for SDM-RTC France, 7.41918E-04 for DDM-RTC France, 7.34782E-04 for TDM-RTC France, 1.59243E-04 for PVM 752, 1.44508E-02 for STP 120/36, 1.72192E-03 for STM 40/36, and 2.05285E-03 for the Photowatt-PWP201 module, respectively. The reliability of IRTHA is futher validated by statistical analyses, including non-parametric tests (Friedman and Wilcoxon rank-sum tests), convergence curve assessments, and graphical representations with boxplots, which collectively confirm its potential to deliver robust and computationally efficient optimization. From the outcomes, it is observed that the IRTHA demonstrates superior performance compared to other existing MH algorithms. The results obtained by IRTHA show exceptional performance in PV system modeling and parameter estimation in solar PV applications.
Sharma et al. (2026) studied this question.