A survey of local search methods for graph coloring

Heuristic algorithms and learning techniques: applications to the graph coloring problem

“…Even today, these TS coloring algorithms are among the most popular and useful coloring techniques for general graph coloring. A historical presentation of Tabucol as well as a comprehensive analysis of some of the best TS algorithms can be found in a recent survey [Galinier and Hertz, 2006]. Experimental comparisons of TS coloring algorithms with other metaheuristic coloring algorithms are presented in [Hoos and Stützle, 2004;.…”

“…One should be aware that there is a vast body of work on graph coloring, so we only touch on the most related publications in this thesis. Indeed, in the last five years, there have been published a number of monographs, surveys and journal special issues [Galinier and Hertz, 2006;Malaguti and Toth, in press;Johnson et al, 2008] with hundreds or even thousands of references to other publications. 6 Furthermore, during the last decade, there have been defended a considerable number of other PhD theses with a major part dedicated to heuristic graph coloring Dorne, 1998;Blöchliger, 2005;Chiarandini, 2005;Devarenne, 2007;Weinberg, 2004;.…”

A Tabu Search Heuristic for the Equitable Coloring Problem

“…Based on experimentation, they suggest to use α = 0.6 and β = 10. Other variants of TabuCol are discussed in [14,17].…”

“…In this work, we propose a new heuristic based on the dynamic TabuCol version of Galinier and Hao [13], one of the best tabu search algorithms for GCP [14]. Then, computational experiments are carried out in order to find the best combination of parameters involved in the dynamic tenure of our heuristic and to show the good performance of it over known benchmark instances.…”

Parallel Simulated Annealing Algorithm for Graph Coloring Problem

“…Most algorithms designed for GCP are iterative heuristics [11], such as genetic algorithms [6], simulated annealing [7,8], tabu or local search techniques [9], minimizing selected cost functions. At the time of this writing, the only parallel metaheuristic for GCP is parallel genetic algorithm [10,[12][13][14][15].…”