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Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization

Authors Ben Rachmut , Roie Zivan , William Yeoh

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Subject Classification

ACM Subject Classification
  • Theory of computation → Distributed algorithms
Keywords
  • Distributed Constraint Optimization Problems
  • Distributed Local Search Algorithms
  • Latency Awareness
  • Multi-Agent Optimization

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Abstract

Researchers recently extended Distributed Constraint Optimization Problems (DCOPs) to Communication-Aware DCOPs so that they are applicable in scenarios in which messages can be arbitrarily delayed. Distributed asynchronous local search and inference algorithms designed for CA-DCOPs are less vulnerable to message latency than their counterparts for regular DCOPs. However, unlike local search algorithms for (regular) DCOPs that converge to k-opt solutions (with k > 1), that is, they converge to solutions that cannot be improved by a group of k agents), local search CA-DCOP algorithms are limited to 1-opt solutions only.
In this paper, we introduce Latency-Aware Monotonic Distributed Local Search-2 (LAMDLS-2), where agents form pairs and coordinate bilateral assignment replacements. LAMDLS-2 is monotonic, converges to a 2-opt solution, and is also robust to message latency, making it suitable for CA-DCOPs. Our results indicate that LAMDLS-2 converges faster than MGM-2, a benchmark algorithm, to a similar 2-opt solution, in various message latency scenarios.

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Ben Rachmut, Roie Zivan, and William Yeoh. Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 24:1-24:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.CP.2024.24

Author Details

Ben Rachmut
  • Ben-Gurion University of the Negev, Beersheba, Israel
Roie Zivan
  • Ben-Gurion University of the Negev, Beersheba, Israel
William Yeoh
  • Washington University in St. Louis, MO, USA

Funding

This research is partially supported by US-Israel Binational Science Foundation (BSF) grant #2018081.

Supplementary Materials

References

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