On Monte Carlo Tree Search for Weighted Vertex Coloring

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 [1] presented at EvoCOP2022.

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

“…They use this algorithm to produce good solutions for instances of Crossword Puzzle Construction and Morpion Solitaire. A more classical problem is tackled by Grelier et al [44]. They combine several dedicated heuristics for the weighted vertex covering problem with Monte Carlo tree search and provide empirical evidence for the advantages and disadvantages of the different algorithmic variants.…”

A Memetic Algorithm with Adaptive Operator Selection for Graph Coloring

“…For the k-col, two particularly interesting local search heuristics are TabuCol [15] and PartialCol [2], which were proposed many years ago. For the WVCP, dedicated and effective local search procedures are much more recent, including AFISA [29], RedLS [30], ILS-TS [22] and TabuWeight [12]. None of these methods really dominates the others for all reference instances of the k-col and the WVCP [2,12,22].…”

“…For the WVCP, dedicated and effective local search procedures are much more recent, including AFISA [29], RedLS [30], ILS-TS [22] and TabuWeight [12]. None of these methods really dominates the others for all reference instances of the k-col and the WVCP [2,12,22]. It would be interesting to choose an appropriate local search to solve each type of instance for each problem.…”

“…We refer the reader to [3,4] for an overview of existing hyperheuristics used to solve various combinatorial optimization problems. More specifically, hyperheuristics have been used to solve partitioning problems, with applications to planning and graph coloring [5,13,27,28]. However, to our knowledge, no hyperheuristic-based memetic algorithm for graph coloring has yet been proposed in the literature.…”

Multi-UAV Urban Logistics Task Allocation Method Based on MCTS

“…In UAV-aided wireless systems, UAV planning problems are addressed using the random-MCTS (R-MCTS) strategy [48]. Furthermore, the greedy-MCTS (G-MCTS) strategy and a combined approach of the random-greedy simulation strategy (random-greedy-MCTS, RG-MCTS) can effectively tackle the weighted vertex coloring problem [49]. However, only some studies leverage MCTS to solve UAV task allocation challenges.…”