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Interpretable Self-Organizing Maps (iSOM) for Visualization of Pareto Front in Multiple Objective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12654))

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Abstract

Visualization techniques in design space exploration with high dimensional data are helpful in enhancing the decision making in the context of multiple objective optimization. Visualization of Pareto solutions obtained is crucial to understand the trade-off between the objectives as it enables intuitive decision making. However, such a task is not trivial beyond three dimensions. In this work, we propose using interpretable self-organizing map (iSOM), to visualize Pareto solutions for MOO problems involving n objectives (\(n>3\)). iSOM enable simplified component plane plots that allow visual inspection of the Pareto fronts and also allow identifying clusters in the Pareto front and the corresponding design variables. Proposed approach is successfully demonstrated on 3 analytical examples.

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This work is supported in part by American Express Lab for Data Analytics, Risk and Technology, Indian Institute of Technology Madras.

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Authors and Affiliations

  1. ADOPT Laboratory, Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India

    Deepak Nagar & Palaniappan Ramu

  2. COIN Laboratory, Computer and Electrical Engineering, Michigan State University, East Lansing, USA

    Kalyanmoy Deb

Authors
  1. Deepak Nagar
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  2. Palaniappan Ramu
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  3. Kalyanmoy Deb
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Corresponding authors

Correspondence to Deepak Nagar , Palaniappan Ramu or Kalyanmoy Deb .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

  3. Southern University of Science and Technology, Shenzhen, China

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Nagar, D., Ramu, P., Deb, K. (2021). Interpretable Self-Organizing Maps (iSOM) for Visualization of Pareto Front in Multiple Objective Optimization. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_51

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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