Compartir
Titel
PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections.
Autor(es)
Schlagwort
PSO-PARSIMONY
t-stub connections
Parsimonious modeling
Auto machine learning
GA-PARSIMONY
Fecha de publicación
2023-09
Verlag
Elsevier
Citación
Divasón, J., Ceniceros, J. F., Sanz-Garcia, A., Pernia-Espinoza, A., & Martinez-de-Pison, F. J. (2023). PSO-PARSIMONY: A method for finding parsimonious and accurate machine learning models with particle swarm optimization. Application for predicting force–displacement curves in T-stub steel connections. Neurocomputing, 548, 126414. https://doi.org/10.1016/j.neucom.2023.126414
Resumen
[EN]We present PSO-PARSIMONY, a new methodology to search for parsimonious and highly accurate models by means of particle swarm optimization. PSO-PARSIMONY uses automatic hyperparameter optimization and feature selection to search for accurate models with low complexity. To evaluate the new proposal, a comparative study with multilayer perceptron algorithm was performed with public datasets and by applying it to predict two important parameters of the force–displacement curve in T-stub steel connections: initial stiffness and maximum strength. Models optimized with PSO-PARSIMONY showed an excellent trade-off between goodness-of-fit and parsimony. The new proposal was compared with GA-PARSIMONY, our previously published methodology that uses genetic algorithms in the optimization process. The new method needed more iterations and obtained slightly more complex individuals, but it performed better in the search for accurate models.
URI
ISSN
0925-2312
Versión del editor
Aparece en las colecciones
- ALF. Artículos [303]
Dateien zu dieser Ressource
Tamaño:
1.642Mb
Formato:
Adobe PDF
Descripción:
Artículo principal