The adaptive noise mechanism was introduced in Novelty+ to automatically adapt noise settings during the search [4]. The local search algorithm G~2WSAT deterministically exploits promising decreasing variables to reduce randomness and consequently the dependence on noise parameters. In this paper, we first integrate the adaptive noise mechanism in G~2WSAT to obtain an algorithm adaptG~2WSAT, whose performance suggests that the deterministic exploitation of promising decreasing variables cooperates well with this mechanism. Then, we propose an approach that uses look-ahead for promising decreasing variables to further reinforce this cooperation. We implement this approach in adaptG~2WSAT, resulting in a new local search algorithm called adaptG~2WSATP. Without any manual noise or other parameter tuning, adaptG~2WSATP shows generally good performance, compared with G~2WSAT with approximately optimal static noise settings, or is sometimes even better than G~2WSAT. In addition, adaptG~2WSATP is favorably compared with state-of-the-art local search algorithms such as R+adaptNovelty+ and VW.
展开▼
