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MACO: A Real-World Inspired Benchmark for Multi-objective Evolutionary Algorithms

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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

The multi-agent coordination (MACO) problem is a real-world inspired multi-objective optimization problem for evolutionary algorithms. It recreates the challenges that are present in optimizing the real-world multi-objective multi-agent pathfinding (MOMAPF) problem. The MACO problem is a scalable, real-valued problem with two objective functions and a known optimal solution. Besides the base version, three variants are proposed, which are based on different properties of the real world MOMAPF problem. Independent sub-problems can be introduced using interaction classes, the multi-modality of the problem can be modified through a set of weights, and the interaction rate between the variables can be altered using the p-norm to approximate the min operator present in the second objective. In our experiments, we assess the performance of three popular multi-objective evolutionary algorithms, both for the basic version and all proposed variations.

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Authors and Affiliations

  1. Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Sebastian Mai, Tobias Benecke & Sanaz Mostaghim

Authors
  1. Sebastian Mai
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  2. Tobias Benecke
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  3. Sanaz Mostaghim
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Corresponding author

Correspondence to Tobias Benecke .

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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Mai, S., Benecke, T., Mostaghim, S. (2023). MACO: A Real-World Inspired Benchmark for Multi-objective Evolutionary Algorithms. 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_22

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  • DOI: https://doi.org/10.1007/978-3-031-27250-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27249-3

  • Online ISBN: 978-3-031-27250-9

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