New methods to color the vertices of a graph
Communications of the ACM
A GRASP for Coloring Sparse Graphs
Computational Optimization and Applications
An Experimental Investigation of Iterated Local Search for Coloring Graphs
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant Focused Crawling Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Computers and Operations Research
Swarming along the evolutionary branches sheds light on genome rearrangement scenarios
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the application of graph colouring techniques in round-robin sports scheduling
Computers and Operations Research
Multi-route railroad blocking problem by improved model and ant colony algorithm in real world
Computers and Industrial Engineering
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
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
Towards objective measures of algorithm performance across instance space
Computers and Operations Research
On the performance of Scatter Search for post-enrolment course timetabling problems
Journal of Combinatorial Optimization
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The focus of this paper is an ant colony optimisation heuristic for the graph colouring problem. We start by showing how a series of improvements enhance the performance of an existing ant colony approach to the problem and then go on to demonstrate that a further strengthening of the construction phase, combined with a tabu search improvement phase, raise the performance to the point where it is able to compete with some of the best-known approaches on a series of benchmark problems.