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Multimodality in Multi-objective Optimization – More Boon than Bane?

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Evolutionary Multi-Criterion Optimization (EMO 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11411))

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Abstract

This paper addresses multimodality of multi-objective (MO) optimization landscapes. Contrary to common perception of local optima, according to which they are hindering the progress of optimization algorithms, it will be shown that local efficient sets in a multi-objective setting can assist optimizers in finding global efficient sets. We use sophisticated visualization techniques, which rely on gradient field heatmaps, to highlight those insights into landscape characteristics. Finally, the MO local optimizer MOGSA is introduced, which exploits those observations by sliding down the multi-objective gradient hill and moving along the local efficient sets.

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Notes

  1. 1.

    If no initial point is given, it will be sampled randomly within the search space.

  2. 2.

    Note that the current implementation of MOGSA only enables the optimization of bi-objective problems.

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Authors and Affiliations

  1. Information Systems and Statistics, ERCIS, University of Münster, Leonardo-Campus 3, 48149, Münster, Germany

    Christian Grimme, Pascal Kerschke & Heike Trautmann

Authors
  1. Christian Grimme
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  2. Pascal Kerschke
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  3. Heike Trautmann
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Corresponding author

Correspondence to Christian Grimme .

Editor information

Editors and Affiliations

  1. Michigan State University, East Lansing, MI, USA

    Kalyanmoy Deb

  2. Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. CINVESTAV-IPN, Mexico, Mexico

    Carlos A. Coello Coello

  4. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  5. University of Jyvaskyla, Jyväskylä, Finland

    Kaisa Miettinen

  6. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Sanaz Mostaghim

  7. Cornell University, Ithaca, NY, USA

    Patrick Reed

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Grimme, C., Kerschke, P., Trautmann, H. (2019). Multimodality in Multi-objective Optimization – More Boon than Bane?. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-12598-1_11

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-12598-1

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