## 6 Conclusions and Future Work  
The hybrid ISSAEDO approach strikes a balance as follows: SSA for the Global Exploration algorithm aids in global exploration. By simulating the foraging behavior of sparrows, SSA enables the simulation of the search space and facilitates the discovery of new regions that potentially harbor optimal solutions. The EDO for Biasing Search algorithm leverages the exponential distribution's properties to direct the search towards locations that are more probable to contain optimal solutions. Adaptive adjustments are made to exponential distribution parameters throughout the optimization procedure.EDO achieves a balance between exploration and exploitation by directing the algorithm towards areas that demonstrate promise. DE enhances candidate solutions iteratively via selection, recombination, and mutation on a population of such solutions. By integrating SSA, EDO, and DE, the ISSAEDO algorithm strikes a harmonious equilibrium between local search and global exploration. Exploration is facilitated by SSA and EDO through the adaptation of the search process in accordance with exponential distribution properties. In contrast, DE places emphasis on local search as a means to enhance and refine the solutions acquired via the remaining components. Adopting a hybrid approach, this method facilitates an exhaustive investigation of the solution space by effectively managing exploration to circumvent local optima and exploitation to enhance solutions within regions that show promise. It is crucial to acknowledge that the precise equilibrium attained by the hybrid approach may be contingent upon parameter configurations, the attributes of the problem, and the interaction among the optimization elements. The hybrid approach must be meticulously crafted and fine-tuned in order to achieve an optimal equilibrium between local and global search within the solution space. This is what our manuscript aims to achieve. According to statistical analysis, the ISSEADO algorithm shows superior performance in terms of convergence speed, accuracy and reliability compared to other widely recognized algorithms. Hence, the proposed methodologies represent appropriate and effective methods for deriving photovoltaic model parameters. In the next plan, the proposed ISSAEDO algorithm will be extended to other equivalent problems, like optimal power flow and maximum power point tracking. Also, the ISSAEDO algorithm will be adjusted to be able to deal with multi-objective PV problems with constraints. Moreover, the ISSAEDO algorithm will be put through its paces by solving more practical models of PV modules. As a comprehensive future outlook, PV technology holds significant importance in technology and scientific research. Its role in providing renewable energy, enhancing energy independence, mitigating climate change, and driving economic growth cannot be overstated. By addressing PV in scientific research, researchers and statisticians can contribute to the advancement of solar cell technology, paving the way for further progress in the field and enabling a sustainable and clean energy future, Overall, continued scientific research and innovation in PV technology are essential for realizing the full potential of solar energy, driving down costs, and ensuring a sustainable and resilient energy future. Author Contributions: Conceptualization S.B. and A.A.A.E.-M.; methodology A.H.A.E.-R. and A.A.A.A.E.-M.; software A.H.A.E.-R. and A.A.A.A.E.-M.; validation A.A.A.E.-M. and A.H.A.E.-R.; formal analysis A.A.-H. and S.B. ; project administration A.A.-H. and S.B. ; funding acquisition A.A.-H. All authors have read and agreed to the published version of the manuscript. Funding: This work is funded by the Federal Ministry of Education and Research of Germany (BMBF) (AutoKoWAT-3 DMAAt Nr. 13N16336) and DFG-Project Nr. GZ: AI 638/15-1. Data Availability Statement: The data presented in this study are available on request from the corresponding authors.