Integrating multi-armed bandit with local search for MaxSAT

Abstract

Partial MaxSAT (PMS) and Weighted PMS (WPMS) are two practical generalizations of the MaxSAT problem. In this paper, we introduce a new local search algorithm for these problems, named BandHS. It applies two multi-armed bandit (MAB) models to guide the search directions when escaping local optima. One MAB model is combined with all the soft clauses to help the algorithm select to satisfy appropriate soft clauses, while the other MAB model is combined with all the literals in hard clauses to help the algorithm select suitable literals to satisfy the hard clauses. These two models enhance the algorithm's search ability in both feasible and infeasible solution spaces. BandHS also incorporates a novel initialization method that prioritizes both unit and binary clauses when generating the initial solutions. Moreover, we apply our MAB approach to the state-of-the-art local search algorithm NuWLS and to the local search component of the incomplete solver NuWLS-c-2023. The extensive experiments conducted demonstrate the excellent performance and generalization capability of the proposed method. Additionally, we provide analyses on the type of problems where our MAB method works well or not, aiming to offer insights and suggestions for its application. Encouragingly, our MAB method has been successfully applied in core local search components in the winner of the WPMS complete track of MaxSAT Evaluation 2023, as well as the runners-up of the incomplete track of MaxSAT Evaluations 2022 and 2023.