## 5 Entanglement and Numerical Test of DE-MPFSC Algorithm Stability
### Entangled Image and Analysis of DE-MPFSC Algorithm  
Since data integration is closely related to data size and data dimension, we first reduces dimensionality of high-dimensional data before performing data experiments. Let the number of population individuals is 1000. The experiment is divided into 5 times on average, and the number of individuals per test is gradually increased from 200. Test runs an average of 5 times. The initial variation factor and initial crossover probability of the algorithm are determined by the literature [19; 20]. The initial mutation factor and the initial crossover probability are respectively set to \(F=0.2,CR=0.5\). In the following experiments, \(F=0.2,CR=0.5\) are dynamically adjusted according to the DE-MPFSC algorithm. The Figures 1-10 below shows the numerical image of the DE-MPFSC algorithm. Figure 1: The entangled image and error of the differential evolution (DE) algorithm running 200 times. Figure 2: The entangled image and error of the Differential Evolution Integration Algorithm Based on Mixed Penalty Function Screening Criteria (DE-MPFSC) algorithm running 200 times. Figure 4: The entangled image and error of the DE-MPFSC algorithm running 400 times. Figure 5: The entangled image and error of the DE algorithm running 600 times. Figure 3: The entangled image and error of the DE algorithm running 400 times. Figure 6: The entangled image and error of the DE-MPFSC algorithm running 600 times. Figures 1-10 above present entanglement degree analysis about the stability and validity between convergence precision and convergence speed of population individuals. First, we analyze the stability and effectiveness of the DE-MPFSC algorithm from the number of population individuals. When the number of individuals are 200, 400, 600, 800, 1000, the DE algorithm entanglement degree about convergence precision and convergence speed are divided into two branches. However, the convergence speed and convergence precision are higher discrete and they  
Figure 8: The entangled image and error of the DE-MPFSC algorithm running 800 times. Figure 10: The entangled image and error of the DE-MPFSC algorithm running 1000 times. Figure 7: The entangled image and error of the DE algorithm running 800 times. Figure 9: The entangled image and error of the DE algorithm running 1000 times. do not converge to the entanglement center point along the convergence curve. The error test rates are 0.1, 0.65, 0.65, 0.55, 1, the correct test rate is 0 and the detection efficiency is 0, which shows that when the population individuals are 200, 400, 600, 800, 1000, the stability of the DE algorithm is not high and it is easy to generate fluctuations. Compared with the DE algorithm, the entanglement degree of the DE-MPFSC algorithm about convergence precision and convergence speed are divided into two branches. The convergence speed and convergence precision are lower discrete and they converge to the entanglement center point along the convergence curve. The error test rates are 0.25, 0.05, 0.05, 0.05, 0.05, the correct test rate are 0.75, 0.95, 0.95, 0.95, 0.95 and the detection efficiency are 3, 19, 19, 19, 19, which shows that when the population individuals are 200, 400, 600, 800, 1000, the stability of the DE-MPFSC algorithm is higher than DE algorithm. In particular, when the number of individuals is 1000, the convergence speed and convergence precision are slightly lower than that of 800 individuals but the distribution is still scattered. They generate a more dramatic marginal differentiation phenomenon on branches and they do not converge to the entanglement center point along the convergence curve. The error test rate is 1, the correct test rate is 0 and the detection efficiency is 0. The situation must not be used as a basis for effective convergence, which shows that when the population individuals is 1000, the stability of the DE algorithm cannot be accepted and it is easy to generate fluctuations.