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Incorporating Preference Information Interactively in NSGA-III by the Adaptation of Reference Vectors

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Evolutionary Multi-Criterion Optimization (EMO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13970))

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Abstract

Real-world multiobjective optimization problems involve decision makers interested in a subset of solutions that meet their preferences. Decomposition-based multiobjective evolutionary algorithms (or MOEAs) have gained the research community’s attention because of their good performance in problems with many objectives. Some efforts have been made to propose variants of these methods that incorporate the decision maker’s preferences, directing the search toward regions of interest. Typically, such variants adapt the reference vectors according to the decision maker’s preferences. However, most of them can consider a single type of preference, the most common being reference points. Interactive MOEAs aim to let decision-makers provide preference information progressively, allowing them to learn about the trade-offs between objectives in each iteration. In such methods, decision makers can provide preferences in multiple ways, and it is desirable to allow them to select the type of preference for each iteration according to their knowledge. This article compares three interactive versions of NSGA-III utilizing multiple types of preferences. The first version incorporates a mechanism that adapts the reference vectors differently according to the type of preferences. The other two versions convert the preferences from the type selected by the decision maker to reference points, which are then utilized in two different reference vector adaptation techniques that have been used in a priori MOEAs. According to the results, we identify the advantages and drawbacks of the compared methods.

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Notes

  1. 1.

    RVs also known as weight vectors or reference points in different MOEAs. To avoid confusion, we will continue to use the term “RVs” only.

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Acknowledgments

This research was supported by the Academy of Finland (grant number 322221). The research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä.

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Authors and Affiliations

  1. University of Jyvaskyla, Faculty of Information Technology, P.O. Box 35 (Agora), FI-40014, University of Jyvaskyla, Finland

    Giomara Lárraga, Bhupinder Singh Saini & Kaisa Miettinen

Authors
  1. Giomara Lárraga
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  2. Bhupinder Singh Saini
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  3. Kaisa Miettinen
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Corresponding author

Correspondence to Giomara Lárraga .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  2. Leiden University, Leiden, The Netherlands

    André Deutz

  3. Leiden University, Leiden, The Netherlands

    Hao Wang

  4. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  5. TH Köln, Gummersbach, Germany

    Boris Naujoks

  6. University of Exeter, Exeter, UK

    Ke Li

  7. University of Jyvaskyla, Jyvaskyla, Finland

    Kaisa Miettinen

  8. De Montfort University, Leicester, UK

    Iryna Yevseyeva

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Lárraga, G., Saini, B.S., Miettinen, K. (2023). Incorporating Preference Information Interactively in NSGA-III by the Adaptation of Reference Vectors. In: Emmerich, M., et al. Evolutionary Multi-Criterion Optimization. EMO 2023. Lecture Notes in Computer Science, vol 13970. Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_41

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  • DOI: https://doi.org/10.1007/978-3-031-27250-9_41

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