## 1 Introduction
### Motivations
Consequently, the matrix devoid of memory documents the outcomes of various solution types, distinguishing between victors and losers. According to the lower memory property, the history of prior failures is disregarded and not retained, as they are seen to be autonomous and unrelated to the present. Therefore, the individuals who did not succeed can contribute to improving future solutions. The proposed EDO method possesses several advantages that distinguish it as a helpful instrument for data analysis, setting it apart from the majority of optimization algorithms. These advantages have motivated us to propose its utilization in this study. The EDO method use numerical optimization methods to efficiently determine the ideal parameters for exponential distribution. The EDO technique demonstrates versatility in its ability to handle diverse datasets and effectively represent various event types that adhere to an exponential distribution. Through the identification of the most suitable parameters for exponential distribution, the optimizer is able to effectively represent the inherent probability distribution of the data. This capability holds significant value in the realm of forecasting and anticipating forthcoming occurrences. The EDO is a widely comprehended and user-friendly probability distribution, wherein the parameters of the distribution possess unambiguous interpretations. An illustration of this concept is rate parameter \(\lambda\), which signifies the anticipated quantity of occurrences during a given timeframe. In contrast, the technique known as Sparrow Search Algorithm (SSA) is a distinctive form of swarm intelligence optimization. The bird's swarm algorithm can be considered a logical extension of the principles obtained from the social interactions and behaviors observed in bird swarms. Sparrows of different species are often known for their sociable nature, which is explained in detail in Section 3.1. The individuals are geographically distributed across many regions worldwide and have a preference for inhabiting areas with human populations. Moreover, many avian species exhibit omnivorous feeding habits, predominantly consuming a diet consisting of weeds or grain seeds. The sparrow population's foraging and anti-predation habits have been influential in the development of their heightened intelligence and resilient memory, distinguishing them from numerous other avian species of smaller size. The Sparrow Search Algorithm balances exploration and exploitation by incorporating random movements and information sharing among sparrows. This allows the algorithm efficient search for the solution space and convergence towards promising solutions.The algorithm combines both global and local search strategies. Sparrows explore new regions in the search space (global search) while also exploiting promising areas (local search). This balanced approach helps in finding optimal solutions.The algorithm's simplicity in terms of implementation is an advantage. It does not require complex mathematical operations, making it accessible for both beginners and researchers, and it has fewer control parameters compared to some other optimization algorithms. This can simplify the tuning process and make the algorithm more user friendly. The ISSAEDO algorithm, which has been recommended, was subjected to evaluation by comparison to several established algorithms across two distinct PV models, namely SDM and DDM. Based on the obtained results, the ISSAEDO algorithm has been identified as a highly competitive meta-heuristic approach. It demonstrates strong performance and competitiveness in terms of escaping local optima and exploring the global optimum.