## 4 Prospects for Research on the Optimization of MOT Parameters
### Several Key Technologies  
To ensure effective parameter optimization, two factors should be focused on:  
1. Complete MOT working model: The working model is crucial for parameter optimization and serves as a key tool for testing the optimization effectiveness. It should accurately represent the forces and motion of atoms in the MOT. The most effective working model is the actual MOT device, as using the real device for parameter optimization yields the most reliable results. However, since the actual MOT is typically coupled with other devices, it is impractical to repeatedly switch the entire testing setup and adjust the control parameters to analyze the trapping performance of the MOT separately. Therefore, the actual device can be used for optimization work only when the optimized performance indicators can be directly detected. Another approach is to create a numerical model of the MOT on the basis of the actual device to simulate atomic motion within the MOT. This method is not limited by the device and allows for easier adjustment of the control parameters with strong repeatability, but it requires ensuring the accuracy of the model. Currently, the working models used in parameter optimization research include practical devices and numerical models.  
2. Efficient multiparameter optimization method: The optimization method is a crucial tool for parameter optimization and directly impacts its efficiency. The more targets and control parameters there are to be optimized and adjusted, the higher the performance requirements for the optimization algorithm. Currently, the dominant optimization method used in optimizing MOT parameters is the control variable method. This method involves analyzing the influence of each control parameter on the optimization objective based on a known model and then determining the optimal value for each control parameter to form the optimal control parameter set. In recent years, advancements in machine learning, artificial intelligence, and other technologies have led to the development of various intelligent algorithms that demonstrate strong computational efficiency and data accuracy in addressing optimization problems, resulting in significant achievements [87].  
The research outputs of each team, including the working models, optimization methods, and performance indicators, are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Years**} & **Research Team Member** & \multirow{2}{*}{**Working Model**} & \multirow{2}{*}{**Optimization Method**} & \multirow{2}{*}{**Performance Index**} \\ \cline{3-3} \cline{5-5}  & & & & \\ \hline
2006 & [PERSON] & Real system, two-level model & Control variable method & Atomic number \\
2016 & [PERSON] & Random number method simulation & Control variable method & Atomic number \\
2017–2019 & [PERSON] & Actual system & Control variable method & Atomic number \\
2018 & [PERSON] & Actual system & Artificial neural network & Optical density \\
2021 & [PERSON] & Monte Carlo simulation based on & Artificial neural network & Atomic number \\ \hline \hline \end{tabular}
\end{table}
Table 3: Review of the research progress on parameter optimization.