Abstract: The performance of convolutional neural network is closely related to the configuration of hyperparameters. However, the selection of optimal hyperparameters is time-consuming and labor-consuming. In order to improve the efficiency of hyperparameter selection, a multi-strategy manta ray foraging optimization algorithm is proposed. On the one hand, half uniform initialization strategy is adopted to improve population diversity. On the other hand, it combines new weight factor update strategy and splitting strategy to improve the convergence speed and fitting accuracy respectively. According to the real coding strategy, this algorithm is applied to the research of convolutional neural network hyperparameter optimization, which can be iterated according to three foraging methods to obtain the optimal hyperparameter configuration. In order to evaluate the effectiveness of hyperparameter optimization, the proposed algorithm is compared with the mainstream convolutional neural network hyperparameter optimization algorithms on mixed national institute of standards and technology and CIFAR-10 datasets. Experimental results show that the proposed algorithm achieves higher accuracy with less resources.

Key words: convolutional neural network, manta ray foraging optimization, hyperparameter optimization