We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm for these problems, called BandMaxSAT, that applies a multi-armed bandit model to guide the search direction. The bandit in our method is associated with all the soft clauses in the input (W)PMS instance. Each arm corresponds to a soft clause. The bandit model can help BandMaxSAT to select a good direction to escape from local optima by selecting a soft clause to be satisfied in the current step, that is, selecting an arm to be pulled. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate that BandMaxSAT significantly outperforms the state-of-the-art (W)PMS local search algorithm SATLike3.0. Specifically, the number of instances in which BandMaxSAT obtains better results is about twice that obtained by SATLike3.0. Moreover, we combine BandMaxSAT with the complete solver TT-Open-WBO-Inc. The resulting solver BandMaxSAT-c also outperforms some of the best state-of-the-art complete (W)PMS solvers, including SATLike-c, Loandra and TT-Open-WBO-Inc.
翻译:部分 MaxSAT (PMS) 和 加权 PMS (WPMS) 是 部分 MaxSAT (PMS) 和 加权 PMS (WPMS) 的两个实用概略, 并提出了这些问题的本地搜索算法, 称为 BandMaxSAT, 用于使用多武装的土匪模型来指导搜索方向。 我们的方法中的土匪与输入(W) PMS 实例中的所有软条款相关。 每个手臂都对应一个软条款。 土匪模型可以帮助 BandMaxSAT 选择一个从本地选择的正确方向, 从而在目前步骤中满足一个软条款, 即选择要拉动的手臂。 我们还为(W) PMS 提出了一个初始方法, 它将单元和二进制条款列为优先。 广泛的实验表明, 土匪MaxSAT 大大超越了输入(W) 的状态(W) PMS SAT 3.0 本地搜索算法。 具体地说, 包Max 获得更好结果的事例大约是SAT3.0 所获得的两倍。 此外, 我们把 BandMax- SAT 和完整 OS- ROP- MS- ROP- MS- MS- ROP- MS- AS- AS- AS- AS- AS- MS- brop- brop- MS- MS- brop- s- str- str- str- MS- str- ex- strupal- AS- MS- MS- MS- MS- MS- ex- AS- MS- MS- MS- MS- AS- AS- AS- MS- MS- AS- AS- str MS- AS- AS- MS- MS- MS- MS- AS- MS- AS- MS- AS- AS- MS- MS- MS- MS- MS- MS- MS- MS- MS- MS- AS- MS- MS- MS- MS- AS- AS- AS- AS- AS- MS- AS- AS- AS- MS- MS-