Using Particle Swarm Optimization with Gradient Descent for Parameter Learning in Convolutional Neural Networks

The use of gradient-based methods are ubiquitously used to update the internal parameters of neural networks. Problems commonly associated with gradient based methods are the tendency for the algorithms to get stuck in sub-optimal local minima, and their slow convergence rate. Efficacious solutions to these issues, such as the addition of “momentum” and adaptive learning rates, have been offered. In this paper, we investigate the efficacy of using particle swarm optimization (PSO) to help gradient-based methods search for the optimal internal parameters to minimize the loss function of a convolutional neural network (CNN). We compare the metric performance of traditional gradient-baseds method with and without the use of a PSO to either guide or refine the search for the optimal weights. The gradient-based methods we examine are stochastic gradient descent with and without a momentum term, as well as Adaptive Moment Estimation (Adam). We find that, with the exception of the Adam optimized networks, regular gradient-based methods achieve better metric scores than when used in conjunction with a PSO. We also observe that using a PSO to refine the solution found through a gradient-descent technique reduces loss better than when using a PSO to dictate that starting solution for gradient descent. Ultimately, the best solution on the MNIST dataset was achieved by the network optimized with stochastic gradient descent and momentum with an average loss score of 0.0092 when evaluated using k-fold cross validation.