  There introduce Particle Optimized Gradient Descent (POGD), an algorithm
based on the gradient descent but integrates the particle swarm optimization
(PSO) principle to achieve the iteration. From the experiments, this algorithm
has adaptive learning ability. The experiments in this paper mainly focus on
the training speed to reach the target value and the ability to prevent the
local minimum. The experiments in this paper are achieved by the convolutional
neural network (CNN) image classification on the MNIST and cifar-10 datasets.
