## 5 Results Discussion and Analysis
### Comparison of Performance of Cell Formation Efficiency with Other Methods  
Ten problems chosen from the literature are used to test the algorithm's performance. The problem size varies from \"5 machines \(\times\) 7 parts\" to \"20 machines \(\times\) 20 parts\". The results from earlier reports are compared with the solution obtained for these problems based on the efficiency of Cell Formation (grouping). The method is run multiple times using the given parameters in order to guarantee a high-quality solution. There are ten runs by default. The part machine incidence matrix is related to the parameters that affect the quality of the solution. A matrix might not be well-structured, making it difficult to classify with optimal effectiveness. The problem size is another consideration. It takes a lot of runs and iterations (generations) to solve an unstructured large-sized problem. These variables determine the number of runs and algorithm parameters. The comparison of Cell Formation (grouping) efficiency is carried out with grouping efficiency (GE) by the following five methods, and the methods were chosen based on the literature: clustering algorithm [38], Genetic Algorithm [39], hybrid algorithm [40], genetic programming [41], and evolutionary algorithm [42].  
Data set problems 1-4, 6, 7, 9, and 10 are taken from [PERSON] and [PERSON] [42], data set problem 5 from [PERSON] and [PERSON] [43], and data set problem 8 from [PERSON], [PERSON], and [PERSON] [40]. The group efficiency (GE) of the above methods is taken from [40; 42; 43]. The problem magnitude, their sources, and the outcomes are displayed in Table 4. Following the completion of the 10 issues, it was discovered that, for the most part, a superior configuration based on GE could be achieved. The results are listed below:  
Figure 11: Formation of Cell 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Machine** & & & **Cell Formation** & & **System** \\ \hline
**Machine No.** & **Utilization** & **Flexibility** & **Cell Formation** & **Utilization** & **Flexibility** & **Productivity** & **Flexibility** \\ \hline
1 & 0.4222 & 0 & Cell 1 & 0.5809 & 0.0547 & 0.0885 & 0.1466 \\
2 & 0.2435 & 0 & Cell 2 & 0.4867 & 0.2457 & & \\
3 & 0.1166 & 0.6534 & Cell 3 & 0.3722 & 0.1854 & & \\
4 & 0.5644 & 0 & Cell 4 & 0.6789 & 0.2377 & & \\
5 & 0.8633 & 0.5878 & Cell 5 & 0.8422 & 0.2244 & & \\
6 & 0.2366 & 0.4932 & & & & & \\
7 & 0.1722 & 0 & & & & \\
8 & 0.9344 & 0 & & & & \\
9 & 0.6788 & 0.8124 & & & & \\
10 & 0.1635 & 0.3544 & & & & \\
11 & 0.3577 & 0 & & & & \\
12 & 0.8511 & 0.1268 & & & & \\
13 & 0.6633 & 0 & & & & \\
14 & 0.9244 & 0 & & & & \\
15 & 0.4446 & 0.2541 & & & & \\ \hline \end{tabular}
\end{table}
Table 14: Results Evaluation.  
Total number of problem sets--10.  
Problem sets with improvements--9 (70%).  
Maximum improvement of GE--8.85 (problem 4).  
Average improvement of 7 problems--4.9.  
Number of problems without improvement--1.  
It is obvious that regardless of the size of the problem, better results are attained.  
This is particularly conspicuous when the data in an ill-structured part machine incidence matrix are difficult to aggregate. The suggested method provides the ideal solution for the ideal problems listed in the literature, when the GE of one is feasible. Most approaches yield the best results when used for ideal or nearly ideal problems, when grouping with a minimal number of exceptional parts and voids is possible. However, those models do not work well with input data that are not organized. In this regard, the suggested algorithm performs fairly well. In this approach, the novel GA based on machine encoding is crucial to obtaining better solutions.