GDBA Revisited: Unleashing the Power of Guided Local Search for Distributed Constraint Optimization

2025-08-09

AI Summary

To address the challenge of local search algorithms easily converging to poor local optima in Distributed Constraint Optimization Problems (DCOPs), this paper proposes the Distributed Guided Local Search (DGLS) framework. DGLS innovatively integrates adaptive violation detection, penalty evaporation, and synchronized update mechanisms. Theoretically, it establishes boundedness of penalty values and the potential game property, thereby mitigating key limitations of traditional Guided Distributed Breakout Algorithm (GDBA)—namely, excessive penalization, unbounded penalty accumulation, and asynchronous updates. By unifying guided search, potential game modeling, and distributed cooperative optimization, DGLS achieves significant performance gains over state-of-the-art methods on standard benchmarks: on structured problems, it outperforms damped Max-Sum by 3.77%–66.3% in solution quality, while demonstrating superior efficiency, robustness, and scalability.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Multiagent Systems: Distributed Problem Solving

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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 ( extbf{3.77%--66.3%}) on structured problems in terms of anytime results.

Problem

• Improving local search for distributed constraint optimization
• Addressing GDBA's poor performance in general-valued problems
• Proposing DGLS with adaptive penalties and synchronization

Innovation

• Adaptive violation condition for high-cost constraints
• Penalty evaporation mechanism for controlled penalization
• Synchronization scheme for coordinated penalty updates