## 1 Introduction
### Motivations  
Numerous algorithms have been proposed for dealing with the issue of obtaining the parameters of the PV model. However, the task of reliably and accurately determining the values of these parameters remains a significant challenge. The hypothesis known as the \"No-Free-Lunch\" principle [7; 24; 25] points that no individual algorithm possesses the capacity to achieve optimal solutions for all optimization issues, hence potentially offering a sufficient explanation to the posed inquiry. This phenomenon arises as a result of the reality that while a methodology may demonstrate exceptional efficacy in addressing a particular problem, it cannot be guaranteed that it will achieve comparable levels of success in addressing other challenges. Therefore, it is imperative to continue conducting research in order to identify an effective meta-heuristic approach. Numerous creative compositions draw inspiration from the \"No-Free-Lunch\" notion, a concept that also permits the adaptation of pre-existing algorithms to address novel problem domains. In this research, a novel strategy called ISSAEDO (Improved Hybrid SSA and EDO with DE technique and bound-constraint adjustment) is proposed for successfully deriving the PV parameter values for multiple PV models, which is discussed in Section 3. The Exponential Distribution Algorithm (EDO) is a proposed method utilized for the determination of optimal parameters according to an exponential distribution, which is explained in detail in Section 3.2. The exponential distribution is a probability distribution that characterizes the inter-event period in a Poisson process when events transpire continuously and autonomously at a consistent average rate. Exponential distribution is characterized by a sole parameter, denoted as \(\lambda\), which represents the rate parameter. In order to maximize exponential distribution, the optimal value for parameter \(\lambda\) that corresponds to the dataset under consideration must be determined. Various optimization techniques, such as maximum likelihood estimation (MLE) or least squares optimization, can be employed to accomplish this task. The MLE method is frequently employed as a means of optimizing exponential distribution. The task at hand is to determine the optimal value of \(\lambda\) that maximizes the likelihood function, which is a mathematical function that characterizes the likelihood of witnessing the supplied data given a specific value of \(\lambda\). Numerical optimization approaches, such as gradient descent or the Newton-Raphson method, can be employed to carry out this task. An alternative method for optimizing the exponential distribution involves the utilization of least squares optimization. This entails the minimization of the total of the squared discrepancies between the actual data and the anticipated values, assuming an exponential distribution with a specified \(\lambda\) value. Numerical optimization algorithms, such as the Levenberg-Marquardt algorithm, can also be employed for this purpose. In general, the proposed EDO demonstrates utility in the context of fitting data to an exponential distribution and determining the optimal parameter value of lambda (\(\lambda\)). The suggested EDO algorithm, shown in Section 3.2 incorporates two distinct approaches: exploitation and exploration. To enhance the current solutions, the approach incorporates three fundamental concepts throughout the exploitation phase: the attribute of memorylessness, the utilization of a guiding solution, and the presence of exponential variance among exponential random variables. To emulate the memory-less characteristic, it is assumed that the initial population solely comprised individuals who were successful and attained high levels of fitness. A distinct matrix, known as memory-less, was constructed to retain the newly generated solutions irrespective of their fitness in relation to the winners from the original population.