###### Abstract
The accuracy achieved on the CIFAR10 dataset after ten epochs, 81.23%, was significantly higher than that of the standard \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Advantages** & **Disadvantages** \\ \hline Grid search method & Simple and easy to implement, suitable for the case of a small number of hyperparameters. & Non-automatic tuning, requires knowledge, not suitable for a large hyperparameter search. \\ \hline Random search method & Simple and easy to implement, can avoid getting trapped in a local optimal solution. & Non-automatic tuning, requires relevant knowledge, effect depends on the distribution of samples, there is a duplicate search problem. \\ \hline PSO-CNN & Automatic tuning, the first introduction of PSO for a hyperparameter search. & \\ \hline LDWPSO-CNN & Automatic tuning, introducing a linear decreasing inertia weighting strategy, balancing the global and local search ability of the algorithm. & Hyperparameter optimization is a complex problem in optimization with multiple local optima. It is essential to note the challenges that metaheuristic algorithms face in dealing with complex optimization problems. Metaheuristic algorithms are prone to falling into local optima, which can lead to a low solution accuracy. \\ \hline DPSO-CNN & Auto-tuning, incorporating distributed technology, reduces the algorithm runtime. & Hyperparameter optimization is a complex problem in optimization with multiple local optima. It is essential to note the challenges that metaheuristic algorithms face in dealing with complex optimization problems. Metaheuristic algorithms are prone to falling into local optima, which can lead to a low solution accuracy. \\ \hline GA-CNN & Automatic optimization, incorporating GA for hyperparameter tuning. & Hyperparameter optimization is a complex problem in optimization with multiple local optima. It is essential to note the challenges that metaheuristic algorithms face in dealing with complex optimization problems. Metaheuristic algorithms are prone to falling into local optima, which can lead to a low solution accuracy. \\ \hline GWO-CNN & Automatic optimization search, incorporating GWO for CNN hyperparameter tuning, better than GA. \\ \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of the hyperparametric optimization algorithms. CNN model and the related hyperparameter optimization algorithms, such as the PSO-CNN, LDWPSO-CNN, and GA-CNN. The essay is organized as follows. The convolutional neural network-related theories and demonstrative CNN models are introduced in Section 2 of this paper as well as the underlying theory and equations of the PSO algorithm. Section 3 details the improvement strategies related to the proposed improved algorithm ADGMPSO. Section 4 tests the proposed algorithm against five mainstream evolutionary algorithms using benchmark test functions and examines the effectiveness of the improved strategy. Section 5 combines the ADGMPSO with two typical CNN models, LeNet-5 and ResNet-18, to perform hyperparameter tuning experiments on the MNIST and CIFAR-10 datasets. Finally, Section 6 provides a summary of the main findings and the conclusions of the study while also identifying the potential avenues for future research.