## 3 Results and Discussion
### Optimization Results  
The optimization method was aimed at improving the control parameters in order to produce the most favorable results. The control parameters were bounded by the maximum and minimum values found in the dataset, as indicated in Table 6. This guaranteed that the optimization process thoroughly investigated the complete range of possible parameter values. The iteration limit was set at 100. The parameter in question determines the maximum number of generations which the algorithm will iterate through in order to find the most optimal solution. A population size of 50 was utilized. This value determines the quantity of potential solutions present in each generation, guaranteeing an ample range of options inside the search area. The convergence tolerance was defined as 0.01. This option defines the level at which the algorithm should terminate if the improvement in objective function values between generations falls below this threshold. The rate of crossover  
Figure 8: Predicted vs. true value test data. Figure 9: Distribution error of test data. recombination was established as 0.8. This parameter governs the likelihood of merging elements from distinct solutions to generate fresh potential solutions, hence promoting the exploration and exploitation of the search space. Figure 10 illustrates the outcomes of the DE optimization. The subplot on the left depicts the progression of weight between generations, while the subplot on the right illustrates the progression of energy use throughout generations. The weight optimization (shown in the left subplot) exhibited an initial quick adjustment followed by slight variations until eventually stabilizing. The initial weight was set at a greater amount and rapidly declined, suggesting efficient exploration of the search space. The ensuing minor fluctuations indicate the algorithm's process of refining as it approached an optimal result. The weight reached a stable state after approximately 30 generations, indicating that the solution selected was either ideal or extremely close to optimal. The energy consumption optimization (right subplot) shows a notable decrease in the first few generations, followed by modest reductions and ultimate stabilization. The energy usage first exhibited a high level and then rapidly declined throughout the first 10 generations, showcasing the algorithm's effectiveness in promptly identifying superior alternatives at the beginning of the process. The algorithm's convergence toward an optimal solution is indicated by the steady reduction and eventual plateau. The stabilization seen after around 20 generations provides confirmation that the DE settings were successful in optimizing energy use. Additionally, because this study employed a full factorial design, optimization was also performed using Minitab software for comparison purposes. A model was created using general factorial regression, resulting in a multiple linear regression equation. To calculate the error with an appropriate degree of freedom, only interactions up to the third order were considered. From the modeling results, the R-squared value obtained for the weight was 99.87%, and for energy consumption, it was 98.37%. The results of both optimizations are presented in Table 8.  
Figure 10: Evolution of generation for weight and energy consumption. This study evaluated the optimization outcomes of injection molding parameters using Minitab and differential evolution (DE) methodologies, comparing them to the original settings. Regarding the melt temperature, DE raised the temperature by 2%, while Minitab indicated a decrease of 1%. The mold temperature modifications had a greater impact with DE, resulting in an 11% rise, while Minitab only showed a 5% increase. The injection speed had a significant surge of 108% when using Minitab, while DE demonstrated more moderate growth of 16%. Both techniques significantly decreased the packing pressure by 51%.