# Personalized-Template-Guided Intelligent Evolutionary Algorithm
## 4 Conclusions  
Existing heuristic algorithms are mostly based on inspiration sources, which are random and unstable. Therefore, based on the optimization principle, this paper proposes a highly stable algorithm named PTG. In particular, PTG can effectively use the knowledge of the particle's own dimension distribution to accelerate the convergence of particle optimization and also effectively use the knowledge of population distribution to help the particle jump out of local optimization. Different from other comparison algorithms, PTG preserves and uses the random particles of the population with constant merit, which helps the algorithm trace back the previous limited points and prevents missing the merit solution. The experimental results show that compared with the other nine comparison algorithms, PTG performs best among 23 classical benchmark functions and CEC2022 benchmark functions, which shows that PTG is suitable for solving multimodal problems and unimodal benchmark functions with global convergence. In addition, PTG has solved four engineering problems, including spring compression design, welded beam structure, pressure vessel, and reducer design, which have practical application value. In addition, PTG ranks first in the Friedman test, which shows that PTG has obvious advantages in two different types of test functions. Although PTG has many advantages, there are still several limitations. Firstly, when dealing with high-dimensional data, the performance of PTG is similar to that of contrast algorithms, which indicates that the dimension distribution \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Algorithm** & \(\mathbf{I_{1}}\) & \(\mathbf{I_{2}}\) & \(\mathbf{d_{1}}\) & **Best** & **Mean** & **Std** \\ \hline PTG & 7.3 & 7.3 & 3.227269460 & 3.3305403487 \(\times\) 10\({}^{4}\) & 3.3305403 \(\times\) 10\({}^{4}\) & 1.4801 \(\times\) 10\({}^{-11}\) \\ WOA & 7.3 & 7.3 & 3.227269518 & 3.3305403487 \(\times\) 10\({}^{4}\) & 3.3312162 \(\times\) 10\({}^{4}\) & 1.03881 \(\times\) 10\({}^{1}\) \\ HHO & 7.3 & 7.3 & 3.227269464 & 3.3305403487 \(\times\) 10\({}^{4}\) & 3.3305410 \(\times\) 10\({}^{4}\) & 3.6 \(\times\) 10\({}^{-2}\) \\ COA & 8.3 & 7.3 & 3.225813184 & 3.3313710127 \(\times\) 10\({}^{4}\) & 3.3511267 \(\times\) 10\({}^{4}\) & 1.4624 \(\times\) 10\({}^{2}\) \\ TSA & 7.3 & 7.3 & 3.227253000 & 3.3305403488 \(\times\) 10\({}^{4}\) & 3.3305405 \(\times\) 10\({}^{4}\) & 2.2 \(\times\) 10\({}^{-3}\) \\ RSA & 7.3 & 8.3 & 3.298115800 & 3.3330883055 \(\times\) 10\({}^{4}\) & 3.3432371 \(\times\) 10\({}^{4}\) & 7.28574 \(\times\) 10\({}^{1}\) \\ FATA & 7.3 & 7.3 & 3.227204486 & 3.3305403493 \(\times\) 10\({}^{4}\) & 3.3305405 \(\times\) 10\({}^{4}\) & 3.6 \(\times\) 10\({}^{-3}\) \\ MFO & 7.3 & 7.3 & 3.227269478 & 3.3305403487 \(\times\) 10\({}^{4}\) & 3.3306053 \(\times\) 10\({}^{4}\) & 3.5549 \\ ECO & 7.3 & 7.3 & 3.227269496 & 3.3305403487 \(\times\) 10\({}^{4}\) & 3.3306053 \(\times\) 10\({}^{4}\) & 3.5549 \\ HO & 7.3 & 7.3 & 3.22726950 & 3.3305403487 \(\times\) 10\({}^{4}\) & 3.3306503 \(\times\) 10\({}^{4}\) & 3.2239 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison results of PTG and advanced algorithm in reducer design. Figure 10: Schematic diagram of reducer design problems. strategy requires a more comprehensive design to handle high-dimensional complex problems. Secondly, real-world problems are intricate and complex. For issues under complex constraints, the PTG algorithm may perform poorly, and all these need to be improved in future work. And in the future, we also plan to further expand the PTG algorithm to solve multi-objective optimization problems and use it to optimize large deep models. Conceptualization, D.H.; Data curation, D.H.; Formal analysis, D.H.; Investigation, D.H.; Methodology, D.H.; Project administration, X.H. ; Resources, D.H.; Software, D.H.; Supervision, X.H. ; Validation, D.H.; Visualization, D.H.; Writing--original draft, D.H.; Writing--review and editing, D.H., X.H., M.G., Y.C. and T.Z.