Improving Local Search for Random 3-SAT Using Quantitative Configuration Checking
Chuan Wen Luo, Kaile Su, Cai Shaowei · Frontiers in artificial intelligence and applications · 2012
Configuration Checking (CC) was proposed as a new diversification strategy for Stochastic Local Search (SLS) algorithm for solving Minimum Vertex Cover, and has been successfully used for solving the Boolean Satisfiability problems, leading to an SLS algorithm called Swcc. However, the CC strategy for SAT is in the early stage of study, and Swcc cannot compete with the best SLS solvers for SAT in SAT Competition 2011. This paper presents a new strategy called Quantitative Configuration Checking (QCC), which is a quantitative version of the CC strategy for SAT. QCC is based on a new definition of “configuration” and works in a different way from the CC strategy does. Specifically, while previous CC strategies work only in the greedy mode, QCC firstly works in the random mode. We use QCC to improve the Swcc algorithm, resulting in a new SLS algorithm for SAT called Swqcc. Experimental results show that the QCC strategy is more effective than the CC strategy. Furthermore, Swqcc outperforms the best local search SAT solver in SAT Competition 2011 called Sparrow2011 on random 3-SAT instances.