Stable Resolving - A Randomized Local Search Heuristic for MaxSAT

Reisch, Julian; Großmann, Peter; Kliewer, Natalia

doi:10.1007/978-3-030-58285-2_12

Cited by 1 publication

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“…There are also some studies using multi-flipping operators for SAT (Mali and Lipen 2003) and MaxSAT (Reisch, Großmann, and Kliewer 2020). But they use an exhaustive method rather than probability sampling in the local search process.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Zhou

2023

AAAI

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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.

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Section: Introductionmentioning

confidence: 99%