A correction to Brelaz's modification of Brown's coloring algorithm
Communications of the ACM
New methods to color the vertices of a graph
Communications of the ACM
A GRASP for Coloring Sparse Graphs
Computational Optimization and Applications
Tabu Search
A New Genetic Local Search Algorithm for Graph Coloring
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Scatter Search for Graph Coloring
Selected Papers from the 5th European Conference on Artificial Evolution
Register allocation & spilling via graph coloring
SIGPLAN '82 Proceedings of the 1982 SIGPLAN symposium on Compiler construction
An adaptive memory algorithm for the k-coloring problem
Discrete Applied Mathematics
An adaptive memory algorithm for the k-coloring problem
Discrete Applied Mathematics
Graph colouring approaches for a satellite range scheduling problem
Journal of Scheduling
Variable space search for graph coloring
Discrete Applied Mathematics
A Critical Element-Guided Perturbation Strategy for Iterated Local Search
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Diversity Control and Multi-Parent Recombination for Evolutionary Graph Coloring Algorithms
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
A search space "cartography" for guiding graph coloring heuristics
Computers and Operations Research
Model and algorithm of multi-depot container truck transportation with time windows
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Computers and Operations Research
MTPSO algorithm for solving planar graph coloring problem
Expert Systems with Applications: An International Journal
A cellular learning automata-based algorithm for solving the vertex coloring problem
Expert Systems with Applications: An International Journal
Coloring large graphs based on independent set extraction
Computers and Operations Research
Population-based and learning-based metaheuristic algorithms for the graph coloring problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Graph coloring with a distributed hybrid quantum annealing algorithm
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
Heuristics for a project management problem with incompatibility and assignment costs
Computational Optimization and Applications
Intensification/Diversification-Driven ILS for a graph coloring problem
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
An exact approach for the Vertex Coloring Problem
Discrete Optimization
Note: Quantum annealing of the graph coloring problem
Discrete Optimization
A New Ant Colony Optimization Algorithm for the Lower Bound of Sum Coloring Problem
Journal of Mathematical Modelling and Algorithms
Automatica (Journal of IFAC)
Improving the extraction and expansion method for large graph coloring
Discrete Applied Mathematics
Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm
Computational Optimization and Applications
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Metaheuristics for robust graph coloring
Journal of Heuristics
Towards objective measures of algorithm performance across instance space
Computers and Operations Research
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Most of the recent heuristics for the graph coloring problem start from an infeasible k-coloring (adjacent vertices may have the same color) and try to make the solution feasible through a sequence of color exchanges. In contrast, our approach (called FOO-PARTIALCOL), which is based on tabu search, considers feasible but partial solutions and tries to increase the size of the current partial solution. A solution consists of k disjoint stable sets (and, therefore, is a feasible, partial k-coloring) and a set of uncolored vertices. We introduce a reactive tabu tenure which substantially enhances the performance of both our heuristic as well as the classical tabu algorithm (called TABUCOL) proposed by Hertz and de Werra [Using tabu search techniques for graph coloring, Computing 1987;39:345-51]. We will report numerical results on different benchmark graphs and we will observe that FOO-PARTIALCOL, though very simple, outperforms TABUCOL on some instances, provides very competitive results on a set of benchmark graphs which are known to be difficult, and outperforms the best-known methods on the graph flat300_28_0. For this graph, FOO-PARTIALCOL finds an optimal coloring with 28 colors. The best coloring achieved to date uses 31 colors. Algorithms very close to TABUCOL are still used as intensification procedures in the best coloring methods, which are evolutionary heuristics. FOO-PARTIALCOL could then be a powerful alternative. In conclusion FOO-PARTIALCOL is one of the most efficient simple local search coloring methods yet available.