Stochastic local search for Partial Max-SAT: an experimental evaluation

Abstract

Stochastic local search (SLS) methods are heuristic-based algorithms that have gained in popularity for their efficiency and robustness when solving very large and complex problems from various areas of artificial intelligence. This study aims to gain insights into SLS methods for solving the Partial Max-SAT (PMSAT) problem. The PMSAT is an NP-Hard problem, an optimization variant of the Propositional Boolean Satisfiability (SAT) problem, that has importance in theory and practice. Many real-world problems including timetabling, scheduling, planning, routing, and software debugging can be reduced to the PMSAT problem. Modern PMSAT solvers are able to solve practical instances with hundreds of thousands to millions of variables and clauses. However, performance of PMSAT solvers are still limited for solving some benchmark instances. In this paper, we present, investigate, and analyze state-of-the-art SLS methods for solving the PMSAT problem. An experimental evaluation is presented based on the MAX-SAT evaluations from 2014 to 2019. The results of this evaluation study show that the currently best performing SLS methods for the PMSAT problem fall into three categories: distinction-based, configuration checking-based, and dynamic local search methods. Very good performance was reported for the dynamic local search based method. The paper gives a detailed picture of the performance of SLS solvers for the PMSAT problem, aims to improve our understanding of their capabilities and limitations, and identifies future research directions.