## 5 Discussion  
This paper introduces a novel approach referred to as ISSAEDO, the hybrid algorithm ISSAEDO combining SSA, EDO, and DE techniques. Hybridization aims to leverage the strengths of individual methods while compensating for their weaknesses, offers strengths and precision in the acquisition of coefficients for PV models. The enhanced global and local search capabilities, robustness to noise and uncertainty, convergence speed, ability to handle complex models, flexibility, general applicability, and integration potential with advanced techniques make this model a valuable tool for researchers and statisticians working on PV system modeling and analysis. One notable characteristic of the SSA, EDO algorithm is its simplicity in parameter specification, requiring only two parameters: population size and stopping criterion. These parameters are commonly found in other meta-heuristic algorithms. As a result, it can be readily applied to a wide range of optimization issues. Furthermore, the DE is employed in the Self-Adaptive, SSA, EDO algorithm to enhance population diversity and facilitate extensive search space exploration, hence preventing the SSA, EDO from converging to a local optimum. In the course of experimental investigations, ISSAEDO is subjected to a comparative analysis with various established meta-heuristic algorithms. The empirical evidence and statistical analysis indicate that ISSAEDO exhibits superior performance compared to other algorithms in the extraction of parameters from PV models, including SDM and DDM, in both practical and statistical contexts. As a result, the performance of the proposed algorithm is evaluated by utilizing two authentic manufacturer's data sheets, namely TFST40 and MCSM55, in order to showcase its practical use. Furthermore, a comparison is made between the measured model data and simulation data acquired by the use of ISSAEDO. The findings indicate a high level of similarity between the extracted data and the model data, suggesting that the unknown parameter values acquired by the current ISSAEDO algorithm are very accurate. Furthermore, the statistical analysis of [PERSON]'s rank-sum test is performed at a significance level of 0.05 in order to evaluate the importance of the reported ISSAEDO in comparison to its counterparts. In conclusion, a comparative analysis is conducted with the proposed ISSAEDO algorithm and the original iterations of DE, SSA, and EDO algorithms in order to demonstrate the enhanced performance achieved by the amalgamation of these methodologies. The RMSE values collected indicate that the suggested ISSAEDO algorithm consistently delivers the lowest RMSE outcomes across all utilized models. The EDO algorithm demonstrates the lowest RMSE values in this study. This illustrates that the described ISSAEDO possesses the capacity to attain solutions that are stable and dependable in comparison to the algorithms of its individual components. ISSAEDO may have potential limitations and compromises. For example, it may exhibit sensitivity to the set of initial solutions, algorithm performance may vary based on the quality and diversity of initial solutions, and poor initialization may lead to convergence to suboptimal solutions. The effectiveness of ISSAEDO may be affected by characteristics of the problem, such as dimensions, multimedia, and the presence of restrictions Like many optimization algorithms, ISSAEDO may have parameters that need to be adjusted for optimal performance. The behavior of the algorithm can be sensitive to the values