Solving DCOPs with Distributed Large Neighborhood Search

The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints. Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search (D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair phase) provides guarantees on solution quality, refining upper and lower bounds during the iterative process, and can exploit domain-dependent structures. Our experimental results show that D-LNS outperforms other incomplete DCOP algorithms on both structured and unstructured problem instances.

近年来，分布式约束优化的领域已经获得了动力，这要归功于其解决与多机构合作相关的各种应用程序的能力。然而，最佳地解决分布式约束优化问题（DCOPS）是NP-HARD。因此，在大规模的复杂应用中，需要不完整的DCOP算法。当前不完整的DCOP算法遭受以下一个或多个限制：他们（a）在不提供质量保证的情况下找到当地的最小值； （b）提供质量宽松的评估；或（c）无法从问题的结构中受益，例如依赖域的知识和硬性约束。因此，利用集中式约束解决社区的策略，我们提出了一个分布式的大型邻里搜索（D-LNS）框架来解决DCOPS。提出的框架（具有新颖的修复阶段）为溶液质量提供了保证，在迭代过程中可以完善上限和下限，并可以利用域依赖性结构。我们的实验结果表明，D-LN在结构化和非结构化问题实例上都优于其他不完整的DCOP算法。