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Satisfiability problem(SAT) is a central problem in artificial intelligence due to its computational complexity and usefulness in industrial applications. Stochastic local search(SLS) algorithms are powerful to solve hard instances of satisfiability problems, among which CScore SAT is proposed for solving SAT instances with long clauses by using greedy mode and diversification mode. In this paper, we present a randomized variable selection strategy to improve efficiency of the diversification mode, and thus propose a new SLS algorithm.We perform a number of experiments to evaluate the new algorithm comparing with the recently proposed algorithms, and show that our algorithm is comparative with others for solving random instances near the phase transition threshold.
Satisfiability problem (SAT) is a central problem in artificial intelligence due to its computational complexity and usefulness in industrial applications. Stochastic local search (SLS) algorithms are powerful to solve hard instances of satisfiability problems, among which CScore SAT is proposed for solving SAT instances with long clauses by using greedy mode and diversification mode. In this paper, we present a randomized variable selection strategy to improve efficiency of the diversification mode, and therefore propose a new SLS algorithm.We perform a number of experiments to evaluate the new algorithm comparing with the recently proposed algorithms, and show that our algorithm is comparative with others for solving random instances near the phase transition threshold.