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Large Neighbourhood Search (LNS) is an algorithmic framework for optimization problems that can yield good performance in many domains. In this paper, we present a method for applying LNS to improve anytime maximum satisfiability (MaxSAT) solving by introducing a neighbourhood selection policy that shows good empirical performance. We show that our LNS solver can often improve the suboptimal solutions produced by other anytime MaxSAT solvers. When starting with a suboptimal solution of reasonable quality, our approach often finds a better solution than the original anytime solver can achieve. We demonstrate that implementing our LNS solver on top of three different state-of-the-art anytime solvers improves the anytime performance of all three solvers within the standard time limit used in the incomplete tracks of the annual MaxSAT Evaluation.

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