Matheuristics for the minimum weighted feedback vertex set and $b$-coloring problems
metaheuristics, feedback vertex set, graph $b$-coloring, mixed integer programming, matheuristics
In this thesis, we propose new matheuristics for two NP-hard graph optimization problems. The first studied problem is denoted the minimum weighted feedback vertex set problem (MWFVS), for which given a weighted graph $G=(V,E)$ it consists in obtaining a minimum weight subset $F\subseteq V$ of the vertex set whose removal makes the graph acyclic. Differently from other approaches in the literature, we tackle this problem via the maximum weighted induced forest problem (MWIF). First, we propose two new compact mixed integer programming (MIP) formulations, using a polynomial number of variables and constraints. Next, we develop a matheuristic that hybridizes a multi-start iterated local search metaheuristic with a MIP-based local search procedure. This thesis also studies the $b$-coloring problem, for which given a graph $G=(V,E)$ it consists in attributing a color to every vertex in $V$ such that adjacent vertices receive different colors, every color has a $b$-vertex, and the number of colors is maximized. A $b$-vertex is a vertex adjacent to vertices colored with all used colors but his own. The optimal solution of the $b$-coloring problem determines the $b$-chromatic number of $G$, denoted $\rchi_b$. We present an integer programming formulation and a very effective multi-greedy randomized heuristic which can be used in a multi-start fashion. Furthermore, a matheuristic is proposed combining the heuristic with the integer programming formulation. Extensive computational experiments show that the proposed techniques for both problems outperform the state-of-art metaheuristics for the majority of tested instances.