# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 4 Results and Discussion
### Numerical Evaluation
Table 4 shows the algorithms that were used to discover the solutions for F13-F20. When optimizing F13, as can be seen, all of the algorithms have roughly the same performance. EJAYA, on the other hand, is regarded as the superior algorithm over other methods for optimizing F14-F16. It is noteworthy that, although EJAYA not only discovers better solutions in F17, it also has a faster convergence rate. EJAYA is also the best method for solving F18 and F19, with values of \(1.14162\)E\(-\)05 and \(1.45847\)E\(+\)01, respectively. Finally, the suggested strategy is less effective than the other algorithms in F20. From this paragraph, it can be stated that EJAYA overcomes the shortcomings of the standard version of JAYA by increasing the population diversity, which results in more dependable and accurate findings than the other algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & **Algorithm** & **Mean** & **STD** & **Worst** & **Best** \\ \hline \multirow{8}{*}{F13} & SCA & 2.18862E+00 & 1.08652E+00 & 2.98211E+00 & 9.98054E\(-\)01 \\ \cline{2-6}  & DA & 3.17541E+00 & 1.62401E+00 & 4.95049E+00 & 9.98004E\(-\)01 \\ \cline{2-6}  & GOA & 9.98004E\(-\)01 & 2.00148E\(-\)16 & 9.98004E\(-\)01 & 9.98004E\(-\)01 \\ \cline{2-6}  & MFO & 2.04105E+03 & 4.45009E+03 & 1.00000E+04 & 6.82542E\(-\)03 \\ \cline{2-6}  & PSO & 3.76477E+00 & 2.01645E+00 & 5.92885E+00 & 1.99203E+00 \\ \cline{2-6}  & CSA & 9.98004E\(-\)01 & 0.00000E+00 & 9.98004E\(-\)01 & 9.98004E\(-\)01 \\ \cline{2-6}  & JAYA & 9.98455E\(-\)01 & 7.86438E\(-\)04 & 9.99822E\(-\)01 & 9.98004E\(-\)01 \\ \cline{2-6}  & EJAYA & 9.98004E\(-\)01 & 2.71948E\(-\)16 & 9.98004E\(-\)01 & 9.98004E\(-\)01 \\ \hline \multirow{8}{*}{F14} & SCA & 4.00116E+01 & 4.78411E+01 & 1.19870E+02 & 1.23403E+00 \\ \cline{2-6}  & DA & 1.85503E+03 & 1.55822E+03 & 4.35763E+03 & 2.88031E+02 \\ \cline{2-6}  & GOA & 1.77073E+00 & 2.99038E+00 & 7.02426E+00 & 5.84282E\(-\)03 \\ \cline{2-6}  & MFO & 6.00002E+03 & 8.94427E+03 & 2.00000E+04 & 1.22123E\(-\)02 \\ \cline{2-6}  & PSO & 1.03821E\(-\)02 & 1.61032E\(-\)02 & 3.82335E\(-\)02 & 6.80767E\(-\)04 \\ \cline{2-6}  & CSA & 3.61472E\(-\)01 & 1.90615E\(-\)01 & 5.70487E\(-\)01 & 1.44379E\(-\)01 \\ \cline{2-6}  & JAYA & 6.06570E\(-\)01 & 6.68239E\(-\)01 & 1.53341E+00 & 1.31801E\(-\)03 \\ \cline{2-6}  & EJAYA & 4.99191E\(-\)04 & 2.28724E\(-\)04 & 8.08109E\(-\)04 & 2.63417E\(-\)04 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The F13–F20 outcomes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & **Algorithm** & **Mean** & **STD** & **Worst** & **Best** \\ \hline \multirow{8}{*}{F12} & SCA & 2.42008E+02 & 5.36482E+02 & 1.20169E+03 & 6.33283E\(-\)01 \\ \cline{2-6}  & DA & 5.15708E+01 & 4.82337E+01 & 1.30150E+02 & 3.91676E+00 \\ \cline{2-6}  & GOA & 6.92309E+00 & 9.24018E\(-\)01 & 8.30938E+00 & 6.02632E+00 \\ \cline{2-6}  & MFO & 6.00004E+03 & 8.94432E+03 & 2.00001E+04 & 2.13515E\(-\)03 \\ \cline{2-6}  & PSO & 2.07338E\(-\)02 & 4.63622E\(-\)02 & 1.03669E\(-\)01 & 2.84415E\(-\)12 \\ \cline{2-6}  & CSA & 5.38686E+00 & 1.52415E+00 & 6.90146E+00 & 2.97901E+00 \\ \cline{2-6}  & JAYA & 8.02845E+00 & 3.70934E+00 & 1.43468E+01 & 4.95010E+00 \\ \cline{2-6}  & EJAYA & 5.85022E\(-\)02 & 3.06103E\(-\)02 & 1.06179E\(-\)01 & 2.41507E\(-\)02 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._