Adaptive Clause Weight Redistribution

In recent years, dynamic local search (DLS) clause weighting algorithms have emerged as the local search state-of-the-art for solving propositional satisfiability problems. However, most DLS algorithms require the tuning of domain dependent parameters before their performance becomes competitive. If manual parameter tuning is impractical then various mechanisms have been developed that can automatically adjust a parameter value during the search. To date, the most effective adaptive clause weighting algorithm is RSAPS. However, RSAPS is unable to convincingly outperform the best non-weighting adaptive algorithm AdaptNovelty, even though manually tuned clause weighting algorithms can routinely outperform the Novelty heuristic on which AdaptNovelty is based. In this study we introduce R+DDFW, an enhanced version of the DDFW clause weighting algorithm developed in 2005, that not only adapts the total amount of weight according to the degree of stagnation in the search, but also incorporates the latest resolution-based preprocessing approach used by the winner of the 2005 SAT competition (R+AdaptNovelty). In an empirical study we show R+DDFW improves on DDFW and outperforms the other leading adaptive (R+AdaptNovelty, R+RSAPS) and non-adaptive (R+GWSAT) local search solvers over a range of random and structured benchmark problems.