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On the Effect of Scalarising Norm Choice in a ParEGO implementation

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Evolutionary Multi-Criterion Optimization (EMO 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10173))

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Abstract

Computationally expensive simulations play an increasing role in engineering design, but their use in multi-objective optimization is heavily resource constrained. Specialist optimizers, such as ParEGO, exist for this setting, but little knowledge is available to guide their configuration. This paper uses a new implementation of ParEGO to examine three hypotheses relating to a key configuration parameter: choice of scalarising norm. Two hypotheses consider the theoretical trade-off between convergence speed and ability to capture an arbitrary Pareto front geometry. Experiments confirm these hypotheses in the bi-objective setting but the trade-off is largely unseen in many-objective settings. A third hypothesis considers the ability of dynamic norm scheduling schemes to overcome the trade-off. Experiments using a simple scheme offer partial support to the hypothesis in the bi-objective setting but no support in many-objective contexts. Norm scheduling is tentatively recommended for bi-objective problems for which the Pareto front geometry is concave or unknown.

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Acknowledgments

This work was supported by Jaguar Land Rover and the UK-EPSRC grant EP/L025760/1 as part of the jointly funded Programme for Simulation Innovation. The authors thank Joshua Knowles for discussions on ParEGO and surrogate-based optimization that helped inspire the research directions in this paper.

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Authors and Affiliations

  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

    Naveed Reza Aghamohammadi, Shaul Salomon, Yiming Yan & Robin C. Purshouse

  2. Department of Mechanical Engineering, Ort Braude College of Engineering, Karmiel, Israel

    Shaul Salomon

Authors
  1. Naveed Reza Aghamohammadi
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  2. Shaul Salomon
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  3. Yiming Yan
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  4. Robin C. Purshouse
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Corresponding author

Correspondence to Robin C. Purshouse .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Heike Trautmann

  2. TU Dortmund University, Dortmund, Germany

    Günter Rudolph

  3. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  4. CINVESTAV-IPN, Mexico City, Mexico

    Oliver Schütze

  5. Clemson University, Clemson, South Carolina, USA

    Margaret Wiecek

  6. University of Surrey, Guildford, United Kingdom

    Yaochu Jin

  7. University of Münster, Münster, Germany

    Christian Grimme

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Aghamohammadi, N.R., Salomon, S., Yan, Y., Purshouse, R.C. (2017). On the Effect of Scalarising Norm Choice in a ParEGO implementation. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-54157-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54156-3

  • Online ISBN: 978-3-319-54157-0

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