###### Abstract  
The configuration of the hyperparameters in convolutional neural networks (CNN) is crucial for determining their performance. However, traditional methods for hyperparameter configuration, such as grid searches and random searches, are time consuming and labor intensive. The optimization of CNN hyperparameters is a complex problem involving multiple local optima that poses a challenge for traditional particle swarm optimization (PSO) algorithms, which are prone to getting stuck in the local optima and achieving suboptimal results. To address the above issues, we proposed an adaptive dimensional Gaussian mutation PSO (ADGMPSO) to efficiently select the optimal hyperparameter configurations. The ADGMPSO algorithm utilized a cat chaos initialization strategy to generate an initial population with a more uniform distribution. It combined the sine-based inertia weights and an asynchronous change learning factor strategy to balance the global exploration and local exploitation capabilities. Finally, an elite particle adaptive dimensional Gaussian mutation strategy was proposed to improve the population diversity and convergence accuracy at the different stages of evolution. The performance of the proposed algorithm was compared to five other evolutionary algorithms, including PSO, BOA, WOA, SSA, and GWO, on ten benchmark test functions, and the results demonstrated the superiority of the proposed algorithm in terms of the optimal value, mean value, and standard deviation. The ADGMPSO algorithm was then applied to the hyperparameter optimization for the LeNet-5 and ResNet-18 network models. The results on the MNIST and CIFAR10 datasets showed that the proposed algorithm achieved a higher accuracy and generalization ability than the other optimization algorithms, such as PSO-CNN, LDWPSO-CNN, and GA-CNN. adaptive; convolutional neural networks; Gaussian mutation; hyperparameter optimization; particle swarm optimization algorithm +
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Early in the research, [PERSON] et al. [10] proposed the grid and random search methods for hyperparameter optimization. The grid search method is an exhaustive trial-and-error approach that requires the appropriate expertise. This method can be effective when the number of hyperparameters is small. However, as the hyperparameter search space increases, the time consumed by the grid search method increases exponentially. The random search method uses sampled parameters for randomly selecting the optimal hyperparameters. The search results have some level of uncertainty, and each sampling point does not consider the previous effects, which may result in the problem of repeated searches. In order to overcome the time-consuming and laborious task of the manual selection of the hyperparameters, researchers have recently achieved promising results using metaheuristic algorithms for hyperparameter optimization. Metaheuristic algorithms have become a research trend in CNN hyperparameter optimization due to their evolutionary features. These algorithms are usually classified into nine different categories, including swarm-based algorithms, chemical-based algorithms, biology-based algorithms, physics-based algorithms, sport-based algorithms, music-based algorithms, social-based algorithms, mathematics-based algorithms, and hybrid methods [11]. Among these categories, swarm-based algorithms are the most widely used in the field of CNN hyperparameter optimization. [PERSON] et al. [12] were the first to apply PSO to the field of CNN hyperparameter tuning and proposed the PSO-CNN algorithm. Their experiments on five different image datasets showed that the proposed algorithm significantly improved the model's accuracy compared to the original AlexNet model. To improve the algorithm's global and local search capabilities, [PERSON] et al. [13] introduced a linear decreasing inertia weighting strategy. They proposed the LDWPSO-CNN algorithm, which obtained a better LeNet-5 image classification accuracy on the MNIST and CIFAR-10 datasets. [PERSON] et al.