1 Guided Local Search Applied to the Sat Problem

Guided Local Search (GLS) has been shown to be successful in solving a number of practical real life problems, such as the travelling salesman problem, radio link frequency assignment problem and the vehicle routing problem. GLS is a penalty-based metaheuristic, which works by augmenting the objective function of a local search algorithm with penalties, to help guide them out of local minima. Our aim is to show that adding GLS to Local Search algorithms generally enhances the performance of such algorithms, which do not include some similar meta-heuristic already. The SAT problem is a class of NP-complete problems. It is known to be important in mathematical logic, constraint satisfaction, VLSI engineering and computing theory. It has recently been the focus of much research on local search algorithms, for example, GSAT and WalkSAT. In this paper, we show progress in applying GLS to local search algorithms along similar lines to GSAT. Results so far show that GLS can reduce the amount of computational effort required to find a solution, when added to such local search algorithms and can also improve the success rate in finding solutions for the local search algorithms.