An Improved Snow Ablation Optimizer for Stabilizing the Artificial Neural Network

Artificial neural networks give more promising and accurate results than other methods for prediction, classification, and segmentation engineering problems. The accuracy of the artificial neural network is affected by the training algorithm used. Gradient-based optimization algorithms are traditional methods to train artificial neural networks. They find an accurate solution to the problem. However, they are sensitive to initial values. It makes them unstable for finding better accuracy results. Moreover, training time becomes higher. To overcome these problems, we proposed an improved snow ablation optimizer (ISAO) algorithm and used it to find the pre-trained weights and biases for initializing the artificial neural network’s weights and biases. Its performance was tested on the MNIST data set and compared with SGDM-BP, SAO, and GOA algorithms. The improved ISAO algorithm achieved better results than compared algorithms regarding cross-entropy, testing, and training accuracy.