## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### Proposed Improved Hybrid SSA with the EDO (ISSAEDO) Algorithm
#### 3.3.3 The Exhaustive ISSAEDO Algorithm  
An improved ISSAEDO technique was produced by combining the SSA algorithm and the EDO algorithm, combined with the DE technique and the bound constraint modification procedure in SSA and EDO. By integrating EDO and DE within the SSA framework, the ISSAEDO algorithm combines the strengths of each technique. EDO helps guide the search based on the properties of exponential distribution, while DE enhances exploration and diversification. Together, these techniques aim to improve the accuracy and efficiency of parameter extraction in solar PV models by efficiently exploring the search space and converging towards optimal solutions. The created ISSAEDO algorithm's pseudo-code may be found in Algorithm 3. In addition, Figure 3 presents a flowchart of the ISSAEDO algorithm so that expected readers can become familiar with the basic processes of the ISSAEDO algorithm. Here is a high-level overview of how the ISSAEDO algorithm integrates EDO and DE within the SSA framework through these steps:  
1. The population of sparrow solutions with random values within the defined search space is initialized. Each solution represents a set of parameters for the solar PV model. 2. The SSA component of the algorithm is responsible for the exploration and exploitation of the search space. It simulates the foraging behavior of sparrows to iteratively improve solutions. 3. The sparrows move within the search space by adjusting their positions based on their current positions, the best solution found so far, and random exploration. 4. The exploration phase allows the algorithm searching for new regions of the search space, while the exploitation phase focuses on intensifying the search around promising solutions. 5. The EDO component utilizes the properties of the exponential distribution to optimize the parameters. 6. The exponential distribution has a probability density function that can be used to model the likelihood of occurrence of events. In the context of optimization, it can guide the search towards regions that are more likely to contain optimal solutions. 7. The EDO component incorporates the exponential distribution into the SSA framework to enhance the exploration and exploitation abilities of the algorithm. 8. The algorithm adapts the parameters of exponential distribution, such as the rate parameter, during the optimization process to improve the convergence towards optimal solutions. 9. The DE component is another optimization technique employed within the ISSAEDO algorithm. 10. DE operates on a population of candidate solutions and iteratively improves them through a combination of mutation, recombination, and selection. 11. During the DE phase, a set of new candidate solutions is generated by perturbing and recombining the existing solutions in the population. 12. The new solutions are then compared with the original solutions, and selection is performed based on their fitness or objective function values. 13. DE introduces additional variation and diversity into the optimization process, allowing for a more thorough exploration of the search space.