Abstract
The satisfiability problem (SAT) is a critically important issue in multiple branches of computer science and artificial intelligence, with its relevance in industrial applications being of particular Significance CCAnr is the current leading stochastic local search (SLS) solver for tackling crafted satisfiable instances. It uses a two-mode strategy, greedy mode and diversification mode. In the present work, we employ a probabilistic selection approach to enhance CCAnr, leading to a new algorithm called ProbCCAnr. Experiments are carried out using the random SAT benchmarks and structured SAT benchmarks including instances encoded from mathematical problems and application problems. The experiments demonstrate that ProbCCAnr significantly improves the performance of state-of-the-art SLS algorithms including CCAnr and ProbSAT, among others. Moreover, ProbCCAnr shows better performance than state of the art complete solvers.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
