Abstract
Stochastic local search (SLS) algorithms, especially those adopting the focused random walk (FRW) framework, have exhibited great effectiveness in solving satisfiable random 3-satisfiability (3-SAT) instances. However, they are still unsatisfactory in dealing with huge instances, and are usually sensitive to the clause-to-variable ratio of the instance. In this paper, we present a new FRW algorithm dubbed FrwCB, which behaves more satisfying in the above two aspects. The main idea is a new heuristic called CCBM, which combines a recent diversification strategy named configuration checking (CC) with the common break minimum (BM) variable-picking strategy. By combining CC and BM in a subtle way, CCBM significantly improves the performance of FrwCB, making FrwCB achieve state-of-the-art performance on a wide range of benchmarks. The experiments show that FrwCB significantly outperforms state-of-the-art SLS solvers on random 3-SAT instances, and competes well on random 5-SAT, random 7-SAT and structured instances.
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Luo, C., Cai, S., Wu, W., Su, K. (2013). Focused Random Walk with Configuration Checking and Break Minimum for Satisfiability. In: Schulte, C. (eds) Principles and Practice of Constraint Programming. CP 2013. Lecture Notes in Computer Science, vol 8124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40627-0_37
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DOI: https://doi.org/10.1007/978-3-642-40627-0_37
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