No abstract

Developing an efficient solver for different NP-complete problems such as propositional satisfiability (SAT) is very complicated and often takes a lot of time. A wide range of problems in different areas of computer science and artificial intelligence can be solved using SAT solvers. The SAT problem is defined as finding a logical assignment that satisfies all clauses in a Boolean formula. The recent developments of different stochastic local search (SLS) SAT solvers present various new heuristics and solving strategies. In this paper, we present an SLS-based SAT solver for structured instances that includes an efficient preprocessing technique along with a few other heuristics. We first remove all equivalence from the SAT formula and then perform searching. Experimental outcomes depict that our new solver can solve some unsolved instances of the state-of-the-art solver; for other benchmarks, our new one also responded quickly.