Optimizing MLP Network Structure for Classification Problems Using PSO and Dominating Vertex Set

Developing high performance neural networks remains a significant challenge in classification problems. This is largely due to the considerable impact of the neural network architecture on its performance. To tackle this challenge, we present a new optimization method based on particle swarm optimization and the dominating vertex set. This method aims to improve model accuracy while reducing the number of neurons and layers through an automated selection process. An experimental evaluation is conducted on several datasets from the UCI Machine Learning Repository. The obtained results show that the proposed method outperforms the multilayer perceptron in terms of classification accuracy.