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

Abstract

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.