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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

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Hybrid Artificial Intelligent Systems (HAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12886))

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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 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.

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Acknowledgement

We are greatly indebted to Banco Santander for the REGI2020/41 fellowship. This study used the Beronia cluster (Universidad de La Rioja), which is supported by FEDER-MINECO grant number UNLR-094E-2C-225.

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Authors and Affiliations

  1. EDMANS Group, Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Julio Fernandez Ceniceros, Alpha Pernia-Espinoza & Francisco Javier Martinez-de-Pison

  2. Department of Mechanical Engineering, University of Salamanca, Bejar, Spain

    Andres Sanz-Garcia

Authors
  1. Julio Fernandez Ceniceros
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  2. Andres Sanz-Garcia
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  3. Alpha Pernia-Espinoza
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  4. Francisco Javier Martinez-de-Pison
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Corresponding author

Correspondence to Francisco Javier Martinez-de-Pison .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Ceniceros, J.F., Sanz-Garcia, A., Pernia-Espinoza, A., Martinez-de-Pison, F.J. (2021). 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. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_2

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  • DOI: https://doi.org/10.1007/978-3-030-86271-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86270-1

  • Online ISBN: 978-3-030-86271-8

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