混合DCOP求解器：提高局部搜索算法的性能
Hybrid DCOP Solvers: Boosting Performance of Local Search Algorithms
摘要：
我们提出了一种新的方法，加快对称和非对称分布式约束优化问题（DCOP）求解器。其核心思想是基于贪婪的快速非迭代DCOP求解器初始化DCOP求解器。这与现有方法相反，在现有方法中，初始化总是使用随机值分配来实现。我们的经验表明，改变现有的DCOP求解器的启动条件，不仅减少了算法的收敛时间高达50%，而且还减少了通信开销，导致更好的解决方案的质量。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。/受试者（混合DCOP）
英文摘要：
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)
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