Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP algorithm.

Meeting Name

2003 IEEE Swarm Intelligence Symposium, 2003. SIS '03


Electrical and Computer Engineering

Keywords and Phrases

BP Algorithm; PSO; Backpropagation; Computational Requirements; Continuous Nonlinear Functions; Convergence; Convergence of Numerical Methods; Evolutionary Algorithms; Evolutionary Computation; Feedforward Neural Nets; Feedforward Neural Network; Neural Network Training; Nonlinear Functions; Optimisation; Particle Swarm Optimization

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2003