# Personalized-Template-Guided Intelligent Evolutionary Algorithm
## 3 Results
### Comparison of PTG with Other Algorithms
#### 3.3.2 Analysis of Experimental Results of CEC2022 Test Function
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & **Metric** & **PTG** & **WOA** & **HHO** & **COA** & **TSA** \\ \hline \multirow{4}{*}{F9} & mean & \(2.48\times 10^{3}\) & \(2.57\times 10^{3}\) & \(2.51\times 10^{3}\) & \(3.48\times 10^{3}\) & \(2.71\times 10^{3}\) \\  & std & \(3.86\times 10^{-2}\) & \(4.82\times 10^{1}\) & \(3.69\times 10^{1}\) & \(3.51\times 10^{2}\) & \(1.62\times 10^{2}\) \\  & rank & 1 & 7 & 4 & 10 & 8 \\  & time & 3.75 & \(2.22\times 10^{-1}\) & \(5.64\times 10^{-1}\) & \(5.37\times 10^{-1}\) & \(2.70\times 10^{-1}\) \\ \hline \multirow{4}{*}{F10} & mean & \(2.68\times 10^{3}\) & \(4.69\times 10^{3}\) & \(3.76\times 10^{3}\) & \(6.24\times 10^{3}\) & \(5.03\times 10^{3}\) \\  & std & \(5.45\times 10^{2}\) & \(1.09\times 10^{3}\) & \(7.84\times 10^{2}\) & \(1.45\times 10^{3}\) & \(1.24\times 10^{3}\) \\  & rank & 1 & 7 & 5 & 10 & 9 \\  & time & \(3.70\) & \(1.91\times 10^{-1}\) & \(5.20\times 10^{-1}\) & \(4.07\times 10^{-1}\) & \(2.30\times 10^{-1}\) \\ \hline \multirow{4}{*}{F11} & mean & \(2.92\times 10^{3}\) & \(3.66\times 10^{3}\) & \(3.10\times 10^{3}\) & \(9.07\times 10^{3}\) & \(6.83\times 10^{3}\) \\  & std & \(1.19\times 10^{2}\) & \(7.79\times 10^{2}\) & \(2.01\times 10^{2}\) & \(5.60\times 10^{2}\) & \(1.11\times 10^{3}\) \\  & rank & 1 & 5 & 4 & 9 & 7 \\  & time & \(3.93\) & \(3.12\times 10^{-1}\) & \(6.82\times 10^{-1}\) & \(6.44\times 10^{-1}\) & \(3.67\times 10^{-1}\) \\ \hline \multirow{4}{*}{F12} & mean & \(2.9471\times 10^{3}\) & \(3.1041\times 10^{3}\) & \(3.1355\times 10^{3}\) & \(3.6458\times 10^{3}\) & \(3.3283\times 10^{3}\) \\  & std & \(6.74\times 10^{1}\) & \(1.21\times 10^{2}\) & \(1.25\times 10^{2}\) & \(3.09\times 10^{2}\) & \(1.87\times 10^{2}\) \\ \cline{1-1}  & rank & 1 & 4 & 7 & 10 & 9 \\ \cline{1-1}  & time & \(3.86\) & \(3.27\times 10^{-1}\) & \(7.76\times 10^{-1}\) & \(7.27\times 10^{-1}\) & \(3.70\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Convergence curves of PTG and 9 optimization algorithms on CEC2022 is shown in Figure 5, where the vertical axis represents the mean value of the algorithm at the same iteration time point during 30 independent runs. Since the algorithm converges very quickly on some functions (8), in order to more obviously show the convergence trend, in function F8, only the first 50 of the 1000 mean points of the algorithm and the solution values corresponding to the 200 th, 400 th, 600 th, 800 th, and 1000 th iterations are taken as X-coordinate points. As can be seen from the figure, among the F2, F3, F9, F10, and F12 functions, compared with other algorithms, PTG has a faster convergence speed. Especially in the F10 function, the algorithm did not stop when it iterated to the 200 th, 400 th, and 600 th generations and could still find better solutions, demonstrating the algorithm's excellent development capabilities. In the F4, F5, F6, and F7 functions, the convergence speed of the algorithm in the early stage is slower than that of some algorithms, but it gradually speeds up in subsequent iterations. When other algorithms stagnated, PTG caught up with and surpassed them, obtaining the optimal solution.