An Improved Particle Swarm Optimization Algorithm for Parameters Optimizing of Feedforward Neural Networks

Abstract

Deep learning is an important branch of neural networks, which has high accuracy in classification and regression problems, and has been widely used. However, its performance is greatly affected by the parameters. In this paper, an improved particle swarm algorithm named as PSO-C is proposed to automatically train the parameters of the feedforward neural networks. In the proposed algorithm, the curiosity factor is introduced to divide the particles into two categories with different curiosity characteristics so as to improve the exploration ability and information mining ability of the particle swarms. At the same time, a chaotic factor is also introduced to avoid the local optimum problem during the neural network’s training. The simulation results show that the PSO-C has better optimization effect on the whole.