GDBA Revisited: Unleashing the Power of Guided Local Search for Distributed Constraint Optimization
This work addresses performance issues in distributed constraint optimization for researchers and practitioners, but it is incremental as it builds upon existing GDBA methods.
The paper tackled the problem of local search algorithms for Distributed Constraint Optimization Problems (DCOPs) converging to poor local optima by proposing Distributed Guided Local Search (DGLS), which achieved competitive performance on general-valued problems and outperformed baselines by 3.77% to 66.3% on structured problems.
Local search is an important class of incomplete algorithms for solving Distributed Constraint Optimization Problems (DCOPs) but it often converges to poor local optima. While GDBA provides a comprehensive rule set to escape premature convergence, its empirical benefits remain marginal on general-valued problems. In this work, we systematically examine GDBA and identify three factors that potentially lead to its inferior performance, i.e., over-aggressive constraint violation conditions, unbounded penalty accumulation, and uncoordinated penalty updates. To address these issues, we propose Distributed Guided Local Search (DGLS), a novel GLS framework for DCOPs that incorporates an adaptive violation condition to selectively penalize constraints with high cost, a penalty evaporation mechanism to control the magnitude of penalization, and a synchronization scheme for coordinated penalty updates. We theoretically show that the penalty values are bounded, and agents play a potential game in our DGLS. Our extensive empirical results on various standard benchmarks demonstrate the great superiority of DGLS over state-of-the-art baselines. Particularly, compared to Damped Max-sum with high damping factors (e.g., 0.7 or 0.9), our DGLS achieves competitive performance on general-valued problems, and outperforms it by significant margins (\textbf{3.77\%--66.3\%}) on structured problems in terms of anytime results.