论文标题
混合DCOP求解器：提高本地搜索算法的性能
Hybrid DCOP Solvers: Boosting Performance of Local Search Algorithms
论文作者
论文摘要
我们提出了一种新的方法来加快对称和不对称分布式约束优化问题（DCOP）求解器。核心思想是基于用贪婪的快速非著作DCOP求解器初始化DCOP求解器的。这与现有的方法相反，在这种方法中，始终使用随机值分配实现初始化。我们从经验上表明，改变现有DCOP求解器的起始条件不仅将算法收敛时间降低到50 \％，而且还会降低通信开销，并带来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所致，这是由DCOP算法激活的扩散模式引起的。） /受试者（Hybrid DCOPS）
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)
