# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 4 Results and Discussion
### Numerical Evaluation
To achieve this, Figure 2 depicts the convergence of all methods for functions 1-6. As shown in F1-F6, the proposed approach outperforms the previous algorithms in terms of the search capability to escape from local minima, convergence, and other factors. The use of quick convergence is highlighted when optimizing real-world optimization problems, where reducing the computational complexity plays a vital role. Similarly, Figure 3 compares all of the methods for functions F7-F12 regarding the convergence rate. The proposed optimization strategy designed to solve F7 is again tagged as the best algorithm. On the other hand, it is classified as the third-best method for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & **Algorithm** & **Mean** & **STD** & **Worst** & **Best** \\ \hline \multirow{8}{*}{F20} & SCA & 3.46460E\(-\)08 & 4.59083E\(-\)09 & 3.78922E\(-\)08 & 3.13998E\(-\)08 \\ \cline{2-6}  & DA & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 \\ \cline{2-6}  & GOA & 3.39673E\(-\)12 & 4.72614E\(-\)12 & 6.73861E\(-\)12 & 5.48450E\(-\)14 \\ \cline{2-6}  & MFO 5.00004E+03 & 7.07102E+03 & 1.00000E+04 & 7.49789E\(-\)02 \\ \cline{2-6}  & PSO & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 & 0.00000E+00 \\ \cline{2-6}  & CSA & 1.11599E\(-\)08 & 1.57825E\(-\)08 & 2.23199E\(-\)08 & 1.09912E\(-\)14 \\ \cline{2-6}  & JAYA & 8.92734E\(-\)10 & 9.63121E\(-\)10 & 1.57376E\(-\)09 & 2.11705E\(-\)10 \\ \cline{2-6}  & EJAYA & 1.73648E\(-\)09 & 2.39446E\(-\)09 & 3.42962E\(-\)09 & 4.33438E\(-\)11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 2: The convergence rate of different algorithms from F1 to F6. solving F8, meaning that it comes after MFO and PSO. It should be noted that, although EJAYA is placed third in F8, its convergence demonstrates a significant improvement compared to its original form. Similarly, while optimizing F9, three algorithms, including the suggested method, basic JAYA, and PSO, are superior. When we glance at F8, we can see that the proposed method is once more classified as the most prominent among these three algorithms, since it can obtain the best solution in fewer iterations. As a result, because it does not become trapped in local minima and seeks to find a better solution throughout the iterations, the suggested technique has provided a good-quality solution in F11. PSO, on the other hand, dominates the proposed method in F12. Nonetheless, EJAYA outperforms the standard version of JAYA and different algorithms in terms of productivity while solving F12. To summarize, it is remarkable that the suggested adjustment improves the performance of the original version of JAYA and dramatically enhances its convergence capabilities. Similarly, Figure 4 depicts the outcomes of all algorithms' convergent capacity in solving F13-F20. To start with, in F13, there are no substantial differences among the algorithms' convergence. Furthermore, for F14-F19, EJAYA is rated as the best algorithm for efficiency and convergence. In F20, on the other hand, its performance was not the top-performing, although its convergence may be suitable. Based on the comparison of several algorithms, it is worth noting that EJAYA is placed third rather than first in certain circumstances. However, as observed, its performance, especially in reaching an optimal solution and improving convergence, improves significantly compared to the original version. Figure 3: The convergence rate of different algorithms from F7 to F12.