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Using Neighborhood-Based Density Measures for Multimodal Multi-objective Optimization

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Book cover Evolutionary Multi-Criterion Optimization (EMO 2021)

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

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Abstract

This paper is about a new approach for dealing with Multimodal Multi-objective Optimization Problems (MMOPs). The major challenge in such problems is to discover several equivalent solutions in the decision space which map to the same values in the objective space. Therefore, it is very important to additionally consider the diversity of the solutions in the decision space which is the goal of this paper. We introduce a new algorithm called NxEMMO which is based on a neighborhood-based density measure in the decision space. We have evaluated our proposed approach on a 14 test problems with 2 to 6 decision variables and 2 and 3 objective functions. The experimental analysis confirms the improvement of the results.

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Authors and Affiliations

  1. Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany

    Mahrokh Javadi & Sanaz Mostaghim

Authors
  1. Mahrokh Javadi
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  2. Sanaz Mostaghim
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Corresponding author

Correspondence to Mahrokh Javadi .

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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Javadi, M., Mostaghim, S. (2021). Using Neighborhood-Based Density Measures for Multimodal Multi-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_27

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_27

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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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