## 5 Entanglement and Numerical Test of DE-MPFSC Algorithm Stability
### Numerical Test and Analysis of DE-MPFSC Algorithm  
Since the data integration is closely related to the data size and data dimension, we set the population individuals to 1000, which is divided into 5 experiments and the average operation is 5 times for each experiment. According to the recommendations of Professor [PERSON] and Professor [PERSON] in Reference [19], the initial mutation factor and initial crossover probability are set to \(F=0.2,CR=0.5\). Tables 1-5 below shows the numerical effects of (Differential Evolution Algorithm Based on Mixed Penalty Function Screening Criteria)DE-MPFSC algorithm and (Differential Evolution)DE algorithm. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{\(DE\)} & \multicolumn{3}{c}{\(DE-MPFSC\)} \\ \cline{2-11} NP & \(F\) & \(CR\) & \(\xi\) & \(\xi\) & \(\varepsilon\%\) & \(F^{*}\) & \(CR^{*}\) & \(\xi\) & \(\xi\) & \(\varepsilon\%\) \\ \hline
200 & 0.1 & 0.9 & 0.8 & 0.5 & \(\pm\)0.625 & 0.1 & 0.9 & 0.12 & 0.05 & \(\pm\)0.416 \\
200 & / & / & 0.73 & 0.66 & \(\pm\)0.904 & 0.5 & 0.6 & 0.20 & 0.06 & \(\pm\)0.3 \\
200 & / & / & 0.81 & 0.49 & \(\pm\)0.604 & 0.3 & 0.2 & 0.19 & 0.11 & \(\pm\)0.578 \\
200 & / & / & 0.75 & 0.54 & \(\pm\)0.72 & 0.2 & 0.3 & 0.15 & 0.03 & \(\pm\)0.2 \\
200 & / & / & 0.8 & 0.56 & \(\pm\)0.7 & 0.2 & 0.3 & 0.15 & 0.02 & \(\pm\)0.13 \\ \hline \hline \multirow{2}{*}{Average} & \multicolumn{3}{c}{\(DE\)} & \multicolumn{3}{c}{\(DE-MPFSC\)} \\ \cline{2-11}  & 0.1 & 0.9 & 0.778 & 0.55 & \(\pm\)0.7106 & 0.26 & 0.46 & 0.162 & 0.054 & \(\pm\)0.3248 \\ \hline \hline \multirow{2}{*}{Variance} & \multicolumn{3}{c}{\(DE\)} & \multicolumn{3}{c}{\(DE-MPFSC\)} \\ \cline{2-11}  & 0 & 0 & 0.00127 & 0.0046 & \(\pm\)0.01407 & 0.023 & 0.083 & 0.00107 & 0.00123 & \(\pm\)0.03164 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical Analysis of Entanglement Degree \(\frac{1}{\xi}\) and Entanglement Degree Error \(\zeta\) of the Differential Evolution (DE) algorithm and the Mixed Penalty Function Screening Criteria (DE-MPFSC) algorithm 200 times. The above five data sheets in Tables 1-5 are data analysis of the entanglement degree \(\frac{1}{\xi}\), entanglement degree error\(\zeta\) and error rate \(\varepsilon\) about convergence precision and convergence speed. We experimented with 200 individuals as the cardinal number, repeating five times per experiment. We record the entanglement degree \(\frac{1}{\xi}\), entanglement degree error \(\zeta\) and error rate \(\varepsilon\) about DE algorithm and DE-MPFSC algorithm in real time. At the same time, we record the dynamic mutation factor \(F\) and the DE-MPFSC algorithm in real time. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{NP} & \multicolumn{4}{c}{\(DE\)} & \multicolumn{4}{c}{\(DE-MPFSC\)} \\ \cline{2-11}  & \(F\) & \(CR\) & \(\xi\) & \(\zeta\) & \(\varepsilon\)\% & \(F^{*}\) & \(CR^{*}\) & \(\xi\) & \(\zeta\) & \(\varepsilon\)\% \\ \hline
800 & 0.1 & 0.9 & 0.63 & 0.44 & \(\pm\)0.698 & 0.1 & 0.9 & 0.15 & 0.015 & \(\pm\)0.1 \\
800 & / & / & 0.64 & 0.31 & \(\pm\)0.484 & 0.5 & 0.4 & 0.27 & 0.03 & \(\pm\)0.11 \\
800 & / & / & 0.75 & 0.50 & \(\pm\)0.667 & 0.3 & 0.3 & 0.33 & 0.06 & \(\pm\)0.182 \\
800 & / & / & 0.70 & 0.50 & \(\pm\)0.714 & 0.2 & 0.3 & 0.30 & 0.017 & \(\pm\)0.057 \\
800 & / & / & 0.71 & 0.36 & \(\pm\)0.507 & 0.2 & 0.3 & 0.27 & 0.02 & \(\pm\)0.074 \\ \hline \multirow{2}{*}{Average} & \multicolumn{4}{c}{\(DE\)} & \multicolumn{4}{c}{\(DE-MPFSC\)} \\ \cline{2-11}  & 0.1 & 0.9 & 0.686 & 0.422 & \(\pm\)0.614 & 0.26 & 0.44 & 0.264 & 0.0284 & \(\pm\)0.1048 \\ \hline \multirow{2}{*}{Variance} & \multicolumn{4}{c}{\(DE\)} & \multicolumn{4}{c}{\(DE-MPFSC\)} \\ \cline{2-11}  & 0 & 0 & 0.00253 & 0.00722 & \(\pm\)0.01205 & 0.023 & 0.068 & 0.00468 & 0.00035 & \(\pm\)0.00231 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numerical Analysis of Entanglement Degree \(\frac{1}{\xi}\) and Entanglement Degree Error \(\zeta\) of the DE algorithm and the DE-MPFSC algorithm 800 times.