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 Fernandez Ceniceros ^[1] ; Andres Sanz-Garcia ^[2] ; Alpha Pernia-Espinoza ^[1] ; Francisco Javier Martinez-de-Pison ^[1]
  1. [1] Universidad de La Rioja

     Universidad de La Rioja

     Logroño, España

     
  2. [2] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
• Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López, Pablo García Bringas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-86271-8, págs. 15-26
• Idioma: inglés
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• Resumen

  • 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.