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 Generalized Distributed Breakout Algorithm (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 DGLS. Extensive empirical results on various benchmarks demonstrate the great superiority of DGLS over state-of-the-art baselines. Compared to Damped Max-sum with high damping factors, our DGLS achieves competitive performance on general-valued problems, and outperforms by significant margins on structured problems in terms of anytime results.

翻译：局部搜索是求解分布式约束优化问题（DCOPs）的一类重要不完全算法，但其常收敛至较差的局部最优解。虽然广义分布式突破算法（GDBA）提供了一套完整的规则集以规避早熟收敛，但在广义值问题上其实际效益仍有限。本文系统性地检视了GDBA，识别出可能导致其性能不佳的三个因素：过于激进的约束违反条件、无界的惩罚累积以及非协调的惩罚更新。针对这些问题，我们提出分布式引导式局部搜索（DGLS）——一种面向DCOPs的新型GLS框架。该框架包含自适应违反条件以选择性地惩罚高成本约束、惩罚蒸发机制以控制惩罚幅度，以及协调惩罚更新的同步方案。我们从理论上证明了DGLS中惩罚值有界，且智能体在该框架下进行势博弈。在多种基准测试上的大量实验结果表明，DGLS显著优于当前最先进的基线方法。相较于采用高阻尼因子的阻尼最大和算法，我们的DGLS在广义值问题上展现出具有竞争力的性能，并在结构化问题的任意时间结果方面以显著优势超越基准方法。