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.
|Journal||Autonomous Agents and Multi-Agent Systems|
|State||Published - Jun 2023|
Bibliographical noteFunding Information:
We thank the anonymous reviewers for their helpful input, and pointers to additional literature, including the work of Aneja and Nair .
© 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