Local Search with Noisy Strategy for Minimum Vertex Cover in Massive Graphs

Abstract

Finding minimum vertex covers (MinVC) for simple undirected graphs is a well-known NP-hard problem. In the literature there have been many heuristics for obtaining good vertex covers. However, most of them focus on solving this problem in relatively small graphs. Recently, a local search solver called FastVC is designed to solve the MinVC problem on real-world massive graphs. Since the traditional best-picking heuristic was believed to be of high complexity, FastVC replaces it with an approximate best-picking strategy. However, since best-picking has been proved to be powerful for a wide range of problems, abandoning it may be a great sacrifice. In this paper we have developed a local search MinVC solver which utilizes best-picking with noise to remove vertices. Experiments conducted on a broad range of real-world massive graphs show that our proposed method finds better vertex covers than state-of-the-art local search algorithms on many graphs.