We propose a novel method for expediting both symmetric and asymmetric Distributed Constraint Optimization Problem (DCOP) solvers. The core idea is based on initializing DCOP solvers with greedy fast non-iterative DCOP solvers. This is contrary to existing methods where initialization is always achieved using a random value assignment. We empirically show that changing the starting conditions of existing DCOP solvers not only reduces the algorithm convergence time by up to 50\%, but also reduces the communication overhead and leads to a better solution quality. We show that this effect is due to structural improvements in the variable assignment, which is caused by the spreading pattern of DCOP algorithm activation.) /Subject (Hybrid DCOPs)

中文翻译：

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混合 DCOP 求解器：提高局部搜索算法的性能

我们提出了一种新方法来加速对称和非对称分布式约束优化问题 (DCOP) 求解器。核心思想基于使用贪婪快速非迭代 DCOP 求解器初始化 DCOP 求解器。这与始终使用随机值分配来实现初始化的现有方法相反。我们的经验表明，改变现有 DCOP 求解器的启动条件不仅可以将算法收敛时间减少高达 50%，而且还减少了通信开销并导致更好的解决方案质量。我们表明这种效果是由于变量分配的结构改进，这是由 DCOP 算法激活的传播模式引起的。）/主题（混合 DCOP）