  A classic graph coloring problem is to assign colors to vertices of any graph
so that distinct colors are assigned to adjacent vertices. Optimal graph
coloring colors a graph with a minimum number of colors, which is its chromatic
number. Finding out the chromatic number is a combinatorial optimization
problem proven to be computationally intractable, which implies that no
algorithm that computes large instances of the problem in a reasonable time is
known. For this reason, approximate methods and metaheuristics form a set of
techniques that do not guarantee optimality but obtain good solutions in a
reasonable time. This paper reports a comparative study of the Hill-Climbing,
Simulated Annealing, Tabu Search, and Iterated Local Search metaheuristics for
the classic graph coloring problem considering its time efficiency for
processing the DSJC125 and DSJC250 instances of the DIMACS benchmark.
