An Improved Hybrid Algorithm Based on PSO and BP for Feedforward Neural Networks

Abstract

In this paper, an improved hybrid algorithm combining particle swarm optimization (PSO) with backpropagation algorithm (BP) is proposed to train feedforward neural networks (FNN). PSO is a global search algorithm, but the swarm in PSO is easy to lose its diversity, which results in premature convergence. On the other hand, BP algorithm is a gradient-descent-based method which has good local search ability around the global minima. Hence, the presented algorithm in this study combines PSO with BP to perform double search. Moreover, in order to improve the diversity of the swarm in the PSO, each particle in the swarm and its best position are disturbed by a random function, and the best position of all particles are reset as the optimum weights of FNN obtained by BP. The proposed algorithm improves the diversity of the swarm as well as reduces the likelihood of the swarm being trapped into local minima on the error surface. Compared with the traditional learning algorithms, the improved learning algorithm has much better convergence accuracy and rate. Finally, the experimental results are given to verify the efficiency and effectiveness of the proposed algorithm.