Latency-Aware 2-Opt Monotonic Local Search for Distributed Constraint Optimization
This work addresses a specific bottleneck in distributed optimization for scenarios with message delays, representing an incremental improvement over prior methods.
The paper tackled the limitation of existing communication-aware distributed constraint optimization algorithms, which only achieve 1-opt solutions, by introducing LAMDLS-2, a monotonic algorithm that converges to 2-opt solutions and shows faster convergence than a benchmark in various latency scenarios.
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.