Fast approximate bi-objective Pareto sets with quality bounds

William Bailey, Judy Goldsmith, Brent Harrison, Siyao Xu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We present and empirically characterize a general, parallel, heuristic algorithm for computing small ϵ-Pareto sets. A primary feature of the algorithm is that it maintains and improves an upper bound on the ϵ value throughout the algorithm. The algorithm can be used as part of a decision support tool for settings in which computing points in objective space is computationally expensive. We use the bi-objective TSP and graph clearing problems as benchmark examples. We characterize the performance of the algorithm through ϵ-Pareto set size, upper bound on ϵ value provided, true ϵ value provided, and parallel speedup achieved. Our results show that the algorithm’s combination of small ϵ-Pareto sets and parallel speedup is sufficient to be appealing in settings requiring manual review (i.e., those that have a human in the loop) or real-time solutions.

Original languageEnglish
Article number5
JournalAutonomous Agents and Multi-Agent Systems
Issue number1
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2022, Springer Science+Business Media, LLC, part of Springer Nature.


  • Applications of MODM
  • Decision support systems
  • Multi-criteria decision making

ASJC Scopus subject areas

  • Artificial Intelligence


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