分布式COPs的异步前向限界
Asynchronous Forward Bounding for Distributed COPs
摘要：
提出了一种求解分布式约束优化问题的新搜索算法。代理按顺序分配变量，并异步计算部分分配的界限。异步边界计算是基于部分赋值的传播。异步前向限界算法（AFB）是一种分布式优化搜索算法，它始终保持一个一致的部分分配。文中详细描述了该算法，并证明了其正确性.实验结果表明，AFB优于同步分支和绑定的许多数量级，并产生一个相变的紧密性的问题的增加。这是一个类似的效果，已经观察到的相变时，本地一致性维护应用于MaxCSP。AFB算法通过增加后跳机制得到进一步增强，从而得到AFB-BJ算法。分布式Backjumping是基于所有值的边界上的累积信息，并同时处理下一个向后移动的候选目标队列。AFB-BJ算法进行了实验比较，其他的DisCOP算法（ADOPT，DPOP，OptAPO），并被证明是一个非常有效的算法的DisCOP。
英文摘要：
A new search algorithm for solving distributed constraint optimization problems (DisCOPs) is presented. Agents assign variables sequentially and compute bounds on partial assignments asynchronously. The asynchronous bounds computation is based on the propagation of partial assignments. The asynchronous forward-bounding algorithm (AFB) is a distributed optimization search algorithm that keeps one consistent partial assignment at all times. The algorithm is described in detail and its correctness proven. Experimental evaluation shows that AFB outperforms synchronous branch and bound by many orders of magnitude, and produces a phase transition as the tightness of the problem increases. This is an analogous effect to the phase transition that has been observed when local consistency maintenance is applied to MaxCSPs. The AFB algorithm is further enhanced by the addition of a backjumping mechanism, resulting in the AFB-BJ algorithm. Distributed backjumping is based on accumulated information on bounds of all values and on processing concurrently a queue of candidate goals for the next move back. The AFB-BJ algorithm is compared experimentally to other DisCOP algorithms (ADOPT, DPOP, OptAPO) and is shown to be a very efficient algorithm for DisCOPs.
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