This work presents the first study of using the popular Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. Starting with the basic MCTS algorithm, we gradually introduce a number of algorithmic variants where MCTS is extended by various simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess the value of each studied combination. We also provide empirical evidence to shed light on the advantages and limits of each strategy.

翻译：这项工作是首次研究使用流行的蒙特卡洛树搜索方法(MCTS),结合解决加权顶点颜色问题的专门超常理论。从基本的 MCTS算法开始,我们逐渐引入了多种算法变量,通过包括贪婪和本地搜索超常学在内的各种模拟战略扩展了MCTS。我们在众所周知的基准实例上进行实验,以评估每个研究过的组合的价值。我们还提供经验性证据,说明每项战略的优点和局限性。