Particle Swarm Optimization for Deep Convolutional Neural Networks

This thesis proposes a hybrid adaptive learning algorithm called Particle Optimized Gradient Descent (POGD) to improve the convergence of convolutional neural networks (CNNs) in image classification. The proposed algorithm has a better protective effect from the local minimum and achieves a higher accuracy with fewer epochs. This research also emphasizes the importance of adjusting the parameters.