Abstract
According to the literature of Particle Swarm Optimization (PSO), there are problems of getting stuck at local minima and premature convergence with this algorithm. A new algorithm is presented in this paper called the Improved Particle Swarm Optimization using the gradient descent method as an operator incorporated into the Algorithm, as a function to achieve a significant improvement. The gradient descent method (BP Algorithm) helps not only to increase the global optimization ability, but also to avoid the premature convergence problem. The Improved PSO Algorithm (IPSO) is applied to the design of Neural Networks to optimize their architecture. The results show that there is an improvement with respect to using the conventional PSO Algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of IEEE International Conference on Neural Networks, IV (IEEE Service Center, Piscataway, NJ, 1995), pp. 1942–1948
R.D. Palupi, M.S. Siti, Particle swarm optimization: technique, system and challenges. Int. J. Appl. Inf. Syst. 1, 19–27 (2011)
Q.H. Bai, Analysis of particle swarm optimization algorithm. Comput. Inf. Sci. 3, 180–184 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Uriarte, A., Melin, P., Valdez, F. (2018). Optimization of Modular Neural Network Architectures with an Improved Particle Swarm Optimization Algorithm. In: Zadeh, L., Yager, R., Shahbazova, S., Reformat, M., Kreinovich, V. (eds) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 361. Springer, Cham. https://doi.org/10.1007/978-3-319-75408-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-75408-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75407-9
Online ISBN: 978-3-319-75408-6
eBook Packages: EngineeringEngineering (R0)