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A New Paradigm in Interactive Evolutionary Multiobjective Optimization

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Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020)

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Abstract

Over the years, scalarization functions have been used to solve multiobjective optimization problems by converting them to one or more single objective optimization problem(s). This study proposes a novel idea of solving multiobjective optimization problems in an interactive manner by using multiple scalarization functions to map vectors in the objective space to a new, so-called preference incorporated space (PIS). In this way, the original problem is converted into a new multiobjective optimization problem with typically fewer objectives in the PIS. This mapping enables a modular incorporation of decision maker’s preferences to convert any evolutionary algorithm to an interactive one, where preference information is directing the solution process. Advantages of optimizing in this new space are discussed and the idea is demonstrated with two interactive evolutionary algorithms: IOPIS/RVEA and IOPIS/NSGA-III. According to the experiments conducted, the new algorithms provide solutions that are better in quality as compared to those of state-of-the-art evolutionary algorithms and their variants where preference information is incorporated in the original objective space. Furthermore, the promising results require fewer function evaluations.

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Notes

  1. 1.

    = 3 (problems) * 7 (objectives) * 4 (generations per iteration) * 4 (iterations).

  2. 2.

    = 9 (problems) * 7 (objectives) * 4 (iterations).

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Acknowledgements

This research was supported by the Academy of Finland (grant numbers 322221 and 311877). 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. Faculty of Information Technology, University of Jyvaskyla, P.O. Box 35 (Agora), 40014, Jyväskylä, Finland

    Bhupinder Singh Saini, Jussi Hakanen & Kaisa Miettinen

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  1. Bhupinder Singh Saini
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  2. Jussi Hakanen
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  3. Kaisa Miettinen
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Correspondence to Bhupinder Singh Saini .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Thomas Bäck

  2. Leiden University, Leiden, The Netherlands

    Mike Preuss

  3. Leiden University, Leiden, The Netherlands

    André Deutz

  4. Sorbonne University, Paris, France

    Hao Wang

  5. Sorbonne University, Paris, France

    Carola Doerr

  6. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  7. University of Münster, Münster, Germany

    Heike Trautmann

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Saini, B.S., Hakanen, J., Miettinen, K. (2020). A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_17

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  • DOI: https://doi.org/10.1007/978-3-030-58115-2_17

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