Resumen de PSO-PARSIMONY: a New Methodology for Searching for Accurate and Parsimonious Models with Particle Swarm Optimization. Application for Predicting the Force-Displacement Curve in T-stub Steel Connections

Julio Fernández Ceniceros, Andrés Sanz García, Alpha Verónica Pernía Espinoza, Francisco Javier Martínez de Pisón Ascacíbar

• 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 by applying it to predict three important parameters of the force-displacement curve in T-stub steel connections: initial stiffness, maximum strength, and displacement at failure. Models optimized with PSO-PARSIMONY showed an excellent trade-off between goodness-of-fit and parsimony. Then, 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.