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

TL;DR

This paper introduces farsighted probabilistic sampling (FPS), a strategy that enhances local search MaxSAT solvers by flipping variable pairs to escape local optima and improve solution quality.

Contribution

The paper proposes FPS, a novel general strategy that replaces single-variable flips with pair flips, boosting local search MaxSAT solver performance and escaping local optima.

Findings

FPS significantly improves state-of-the-art (W)PMS solvers.

FPS generalizes well across various local search MaxSAT algorithms.

Extensive experiments confirm the effectiveness of FPS.

Abstract

Local search has been demonstrated as an efficient approach for two practical generalizations of the MaxSAT problem, namely Partial MaxSAT (PMS) and Weighted PMS (WPMS). In this work, we observe that most local search (W)PMS solvers usually flip a single variable per iteration. Such a mechanism may lead to relatively low-quality local optimal solutions, and may limit the diversity of search directions to escape from local optima. To address this issue, we propose a general strategy, called farsighted probabilistic sampling (FPS), to replace the single flipping mechanism so as to boost the local search (W)PMS algorithms. FPS considers the benefit of continuously flipping a pair of variables in order to find higher-quality local optimal solutions. Moreover, FPS proposes an effective approach to escape from local optima by preferring the best to flip among the best sampled single variable and the best sampled variable pair. Extensive experiments demonstrate that our proposed FPS strategy significantly improves the state-of-the-art (W)PMS solvers, and FPS has an excellent generalization capability to various local search MaxSAT solvers.