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Local search has been demonstrated as an efficient approach for two practicalgeneralizations of the MaxSAT problem, namely Partial MaxSAT (PMS) and WeightedPMS (WPMS). In this work, we observe that most local search (W)PMS solversusually flip a single variable per iteration. Such a mechanism may lead torelatively low-quality local optimal solutions, and may limit the diversity ofsearch directions to escape from local optima. To address this issue, wepropose a general strategy, called farsighted probabilistic sampling (FPS), toreplace the single flipping mechanism so as to boost the local search (W)PMSalgorithms. FPS considers the benefit of continuously flipping a pair ofvariables in order to find higher-quality local optimal solutions. Moreover,FPS proposes an effective approach to escape from local optima by preferringthe best to flip among the best sampled single variable and the best sampledvariable pair. Extensive experiments demonstrate that our proposed FPS strategysignificantly improves the state-of-the-art (W)PMS solvers, and FPS has anexcellent generalization capability to various local search MaxSAT solvers.

Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT Solvers