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Automatic Design of Multi-objective Particle Swarm Optimizers

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Swarm Intelligence (ANTS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13491))

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Abstract

Multi-objective particle swarm optimizers (MOPSOs) have been widely used to deal with optimization problems having two or more conflicting objectives. As happens with other metaheuristics, finding the most adequate parameters settings for MOPSOs is not a trivial task, and it is even harder to choose structural components that determine the algorithm’s design. Thus, it is an open question whether automatically-designed MOPSOs can outperform the best human-designed MOPSOs from the literature. In this paper, we first design and develop a component-based architecture and an algorithmic template, called AMOPSO, for the auto-design and auto-configuration of MOPSOs using jMetal and we integrate it with irace, an automatic-configuration tool. Second, by taking as our starting point two algorithms (OMOPSO and SMPSO), we conduct a study focused on automatically generating three AMOPSO variants by using different well-known multi-objective benchmarking problem families (ZDT, DTLZ, and WFG) as training problems for automatic design, and then we analyze whether they improve upon the initial versions of the algorithms and how their components differ. Experiments show that the two AMOPSO variants obtained from using, respectively, the ZDT and DTLZ problems for training are able to statistically outperform the SMPSO and OMOPSO algorithms in all three benchmark families previously indicated.

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Acknowledgements

This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE) and the Andalusian PAIDI program with grant P18-RT-2799. M. López-Ibáñez is a “Beatriz Galindo” Senior Distinguished Researcher (BEAGAL 18/00053) funded by the Spanish Ministry of Science and Innovation (MICINN). Carlos A. Coello Coello gratefully acknowledges support from CONACyT grant no. 2016-01-1920 (Investigación en Fronteras de la Ciencia 2016).

Author information

Authors and Affiliations

  1. ITIS Software, Universidad de Málaga, Málaga, Spain

    Daniel Doblas, Antonio J. Nebro & José García-Nieto

  2. Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, Málaga, Spain

    Antonio J. Nebro & José García-Nieto

  3. Alliance Manchester Business School, University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  4. Departamento de Computación, CINVESTAV-IPN, Mexico City, Mexico

    Carlos A. Coello Coello

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  1. Daniel Doblas
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  2. Antonio J. Nebro
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  3. Manuel López-Ibáñez
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  4. José García-Nieto
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  5. Carlos A. Coello Coello
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Corresponding author

Correspondence to Antonio J. Nebro .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  3. University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  4. University of Málaga, Málaga, Spain

    José García-Nieto

  5. Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  6. Worcester Polytechnic Institute, Worcester, MA, USA

    Carlo Pinciroli

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

  8. Université Libre de Bruxelles, Brussels, Belgium

    Christian Camacho-Villalón

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Doblas, D., Nebro, A.J., López-Ibáñez, M., García-Nieto, J., Coello Coello, C.A. (2022). Automatic Design of Multi-objective Particle Swarm Optimizers. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2022. Lecture Notes in Computer Science, vol 13491. Springer, Cham. https://doi.org/10.1007/978-3-031-20176-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-20176-9_3

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