Local Search For SAT Research Articles

Configuration Checking with Aspiration in Local Search for SAT

An interesting strategy called configuration checking (CC) was recently proposed to handle the cycling problem in local search for Minimum Vertex Cover. A natural question is whether this CC strategy also works for SAT. The direct application of CC did not result in stochastic local search (SLS) algorithms that can compete with the current best SLS algorithms for SAT. In this paper, we propose a new heuristic based on CC for SLS algorithms for SAT, which is called configuration checking with aspiration (CCA). It is used to develop a new SLS algorithm called Swcca. The experiments on random 3-SAT instances show that Swcca significantly outperforms Sparrow2011, the winner of the random satisfiable category of the SAT Competition 2011, which is considered to be the best local search solver for random 3-SAT instances. Moreover, the experiments on structured instances show that Swcca is competitive with Sattime, the best local search solver for the crafted benchmark in the SAT Competition 2011.

Weight Resets in Local Search for SAT

Weight Resets in Local Search for SAT

A Switching Criterion for Intensification and Diversification in Local Search for SAT12

We propose a new switching criterion, namely the evenness or unevenness of the distribution of variable weights, and use this criterion to combine intensification and diversification in local search for SAT. We refer to the ways in which state-of-the-art local search algorithms adaptG2 W SATP and V W select a variable to flip, as heuristic adaptG2 W SATP and heuristic V W , respectively. To evaluate the effectiveness of this criterion, we apply it to heuristic adaptG2 W SATP and heuristic V W , in which the former intensifies the search better than the latter, and the latter diversifies the search better than the former. The resulting local search algorithm, which switches between heuristic adaptG2 W SATP and heuristic V W in every step according to this criterion, is called Hybrid. Our experimental results show that, on a broad range of SAT instances presented in this paper, Hybrid inherits the strengths of adaptG2 W SATP and V W , and exhibits generally better performance than adaptG2 W SATP and V W . In addition, Hybrid compares favorably with state-of-the-art local search algorithm R+adaptNovelty+ on these instances. Furthermore, without any manual tuning parameters, Hybrid solves each of these instances in a reasonable time, while adaptG2 W SATP , V W , and R+adaptNovelty+ have difficulty on some of these instances.