  Convolutional neural networks (CNNs) are widely used in image recognition.
Numerous CNN models, such as LeNet, AlexNet, VGG, ResNet, and GoogLeNet, have
been proposed by increasing the number of layers, to improve the performance of
CNNs. However, performance deteriorates beyond a certain number of layers.
Hence, hyperparameter optimisation is a more efficient way to improve CNNs. To
validate this concept, a new algorithm based on simplified swarm optimisation
is proposed to optimise the hyperparameters of the simplest CNN model, which is
LeNet. The results of experiments conducted on the MNIST, Fashion MNIST, and
Cifar10 datasets showed that the accuracy of the proposed algorithm is higher
than the original LeNet model and PSO-LeNet and that it has a high potential to
be extended to more complicated models, such as AlexNet.
