The neural architecture search(NAS) technique automatically finds the optimal combination and connectivity of layers in a neural network, as well as the optimal distribution of various hyperparameters. The method generated a number of different convolutional neural network(CNN) from the search space, and used a hybrid particle swarm optimization(HPSO) algorithm to treat a certain number of neural network individuals as a population and the performance of each individual under the evaluation metric as the fitness. Within a given number of generations, each neural network individual learnt its own historical best fitness individual, and the best fitness individual of the whole population, and iteratively improved its own network architecture. Experimental results show that the optimal network architecture emerging from the algorithm runs achieves competitive results in terms of the trade-off between the number of network parameters and accuracy on multiple benchmark datasets for the image classification task, compared to both the hand-designed neural network and the genetic algorithm-based NAS algorithm.

[2023-03-08] Accepted Paper /doc/2022.12.0811/v1

[2023-07-05] Printed Article /doc/2022.12.0811/v2