###### Abstract
[14] proposed the DPSO-CNN model, which combines distributed techniques with PSO-CNN to reduce the time required for algorithm operation. [PERSON] et al. [15] addressed the problem of the algorithm runtime by proposing a multi-level particle swarm optimization (MPSO-CNN) algorithm, which combined the hierarchical ideas with the hyperparameter optimization problems by simultaneously searching for the structure and hyperparameters of the CNN using multiple levels of particle swarms. [PERSON] et al. [16] applied a genetic algorithm to the hyperparameter optimization problem of convolutional neural networks and achieved superior results in their experiments on an amyloid brain dataset for Alzheimer's diagnosis by searching for an excellent CNN network structure and hyperparameters. [PERSON] et al. [17] used the gray wolf optimization algorithm to select the suitable CNN hyperparameters by searching for them in a skin lesion multiclass dataset. They conducted experiments with GA-CNN model, which showed an excellent competitiveness for the proposed algorithm. The cited studies in the references [12; 13; 14; 15; 16; 17] demonstrated the effective outcomes in hyperparameter optimization of convolutional neural networks by applying evolutionary algorithms. Nonetheless, CNN hyperparameter optimization is a complex optimization problem with multiple locally optimal solutions, and these studies have disregarded the limitations of evolutionary algorithms in solving intricate optimization problems. Specifically, evolutionary algorithms converge on locally optimal solutions and have a limited solution accuracy when confronted with complex issues. The advantages and disadvantages of the mentioned hyperparametric optimization methods are shown in Table 1.  
To address the challenges mentioned above and improve the automatic discovery of the optimal hyperparameter configurations, this paper presents a particle swarm algorithm with an adaptive dimensional Gaussian mutation. Compared to the original PSO, this method offers three key advantages: (1) the initialization of the population using cat chaos mapping, which enhances the uniformity of the initial population; (2) the introduction of a sine-based nonlinear decreasing inertia weight and a heterogeneous learning factor strategy that balances the pre-exploration and post-exploitation capabilities; and (3) the proposal of a strategy for an adaptive dimensional Gaussian mutation for the elite particles to increase the optimal global particle, which enhances the search range and facilitates the escape from local optimal solutions. The elite particles' dimensionality is adaptively reduced later to preserve most of their information and improve the algorithmic convergence accuracy. The experimental results demonstrate the superiority of the proposed algorithm over the standard CNN models and the PSO-CNN, LDWPSO-CNN, and GA-CNN methods. The main contributions of this paper are as follows:  
1. This paper proposes an adaptive dimensional Gaussian mutation particle swarm algorithm to enhance the algorithm's performance by addressing the limitations of the standard PSO method. The proposed approach leverages a cat chaotic initial population, a sine-based nonlinear decreasing inertia weight, an asynchronous learning factor strategy, and an elite particle adaptive dimensional Gaussian mutation strategy. 2. The performance of the proposed algorithm is evaluated through benchmark function comparisons with the mainstream evolutionary algorithms. Additionally, a single policy ablation experiment is conducted to demonstrate the effectiveness of the proposed improvements. 3. The proposed algorithm is applied to the hyperparameter optimization for the classical CNN models LeNet-5 and ResNet-18 on the MNIST and CIFAR10 datasets, respectively. The experimental results demonstrate that the optimized network model achieved a 99.11% accuracy after only five epochs on the MNIST dataset and 81.23% after ten epochs on the CIFAR10 dataset.