###### Abstract
The considered TDM doesn't consider the recombination defects in the solar cell. * An extensive review presenting the available approaches for the PV parameter estimation was presented in [33]. * In [34], an interval branch and bound optimization algorithm were proposed for parameter estimation of three photovoltaic models while the EHO was presented for extracting the parameters using three Perovskite solar cell models which represent the third generation of solar cells [35]. * In [36], the forensic optimization algorithm was developed for parameter extraction of three solar cell models. In the previous survey, the main models that represent solar cells as single-, double, and triple diode models were reported. Several optimization methods were developed to find the optimal parameters of each model. The number of variables varied from five unknowns to nine unknowns. Finding both a suitable model and the best parameters of the suggested model is still an open research area. In this regard, this paper is concerned with dealing with both aspects: finding a more accurate model by proposing a modified three diode model and proposing two soft paradigms for finding the optimal parameters of the modified three diode model (MTDM) that is proposed for the first time in this work. The MTDM offers a more accurate physical representation for different solar cell types under different operation conditions. Therefore, these models are efficient in dealing with the natural characteristics of the MCS solar cell. In this work, the proposed models are investigated based on experimental data reported in the previous literature. In the direction of soft computing, two methods are considered in the current work. A well-established particle swarm optimization (PSO) version is presented for the parameter estimation of the proposed models. The proposed version is developed to speed up the convergence to reduce the number of iterations and to enhance the overall solution quality. Added to that, it continuously modulates the inputs of each iteration with the aid of the fitness and control variables obtained from the previous iteration. The closed-loop particle swarm optimization (CLPSO) is proposed in this study. Also, anew swarm search-based method called elephant herding optimization (EHO), which mimics the behavior of elephant groups, is tested in this paper. The EHO algorithm finds much fitter solutions compared to DE, GA, and BBO for most benchmark problems [37]. This study provides a bi-dimensional assessment study. The first one assesses the proposed MTDM versus MDDM, while the second dimension represents an assessment of the quality of the solutions obtained by the two soft computing paradigms. The main contributions of the current study can be summarized as follows:  
* The novel application of two soft computing techniques for estimating the parameter of MSC solar cell models. * The proposed novel pattern of the MSC solar cell with a modified three diode model. * The performance evaluation of two competitive optimization algorithms. * The simulation results in high accuracy of the proposed MTDM, which are assessed with DDM and MDDM. The coming sections of this work are ordered as follows: Section 2 illustrates the photovoltaic (PV) models. The parameter estimation problem formulation is presented in Section 3. Section 4 presents the proposed soft parameter estimation paradigms CLPSO and EHO. Section 5 describes the evaluation, simulation, and results which will be discussed. Finally, Section 6 of this paper contains its conclusions and outcome.