## 5 Experimental Results and Analysis
### Scheduling Results and Analysis  
The 20 production order processing data in Table 1 were solved by the IGA, and the resulting Gantt chart is shown in Figure 8.  
In the production experiment platform of the knitting workshop, after the production orders were finished by the M1 to M8 circular knitting machines, the M9 fabric transport AGV completed the material handling. At this time, the full fabric rollers dropped out and waited for 2 min for the empty fabric rollers to be placed before the circular knitting machines could continue to arrange the knitting production orders. In Figure 8, the M1 circular knitting machine completes process 2 of the 18 th production order and then sets aside 2 min before starting to process the 10 th production order.  
In order to verify the algorithmic characteristics of the proposed IGA based on tabu search for the production scheduling problem in the knitting workshop, the 20 production orders in Table 1 were taken for comparison experiements using the GWO, GA and IGA, and the maximum number of iterations was 50. Then, the value of the fitness function for each iteration was recorded, and the laboratory results are plotted on a line graph as shown in Figure 9.  
Figure 8: The Gantt chart of the improved genetic algorithm validation.  
Figure 9: The Gantt chart of the improved genetic algorithm validation.  
Among these results, the green curve represents the GWO, the blue curve represents the traditional GA and the red curve represents the IGA. Comparing the three genetic algorithms, the GWO has a slow convergence speed, the solution accuracy is poor and it does not perform well in the large-scale order production scheduling in the knitting workshop. Meanwhile, both the GWO and the GA are prone to local optima, while the IGA not only has an excellent performance in the convergence speed of the algorithm, but also the global search ability and local search ability are well improved.  
Considering the randomness of the algorithm, the comparison experiment was repeated 10 times for the traditional GA and the IGA. The value of the optimal fitness function and the minimum number of iterations to reach the optimal solution were recorded, and the line graph was plotted as shown in Figure 10, where the red curve represents the IGA and the blue curve represents the traditional GA.  
Through 10 groups of algorithm comparison experiments, it can be obtained from Figure 10 that the traditional GA reaches the optimal fitness value of 399 after 39 iterations on average, while the IGA based on tabu search can reach the optimal fitness value of 396 after 29 iterations on average, and the convergence speed is improved by about 25%. Compared with the traditional algorithm, the IGA not only performs well in the convergence speed at the beginning of the iteration and the stability of the algorithm solution, but also performs better in the overall convergence speed and the global search ability of the algorithm.  
In summary, the IGA based on tabu search algorithm can effectively meet the production scheduling requirements of the knitting workshop and effectively improve the workshop production and processing efficiency.