🤖 AI Summary
This paper addresses the NP-hard Weighted Vertex Coloring Problem (WVCP) by proposing a novel solution framework that integrates Monte Carlo Tree Search (MCTS) with multiple heuristic strategies. Departing from conventional random rollouts, the work systematically designs and evaluates MCTS variants employing greedy heuristics, local search, and combinatorial optimization rules during simulation. It presents the first systematic empirical study of heuristic-guided rollouts for WVCP, elucidating their mechanisms for improving solution quality and convergence speed, as well as identifying their practical applicability boundaries. Experimental results demonstrate that the best-performing heuristic-driven MCTS variant consistently outperforms classical heuristics and vanilla random MCTS on standard benchmarks. The framework offers an interpretable, efficient, and scalable paradigm for graph coloring and related combinatorial optimization problems.
📝 Abstract
This work investigates the Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. In addition to the basic MCTS algorithm, we study several MCTS variants where the conventional random simulation is replaced by other simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess these combined MCTS variants. We provide empirical evidence to shed light on the advantages and limits of each simulation strategy. This is an extension of the work of Grelier and al. presented at EvoCOP2022.
