Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Almost all k-colorable graphs are easy to color
Journal of Algorithms
New methods to color the vertices of a graph
Communications of the ACM
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Finding the chromatic number by means of critical graphs
Journal of Experimental Algorithmics (JEA)
Simulated Annealing and Graph Colouring
Combinatorics, Probability and Computing
A survey of local search methods for graph coloring
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Center particle swarm optimization
Neurocomputing
Constructive generation of very hard 3-colorability instances
Discrete Applied Mathematics
An improved ant colony optimisation heuristic for graph colouring
Discrete Applied Mathematics
Aggregation pheromone density based data clustering
Information Sciences: an International Journal
An adaptive flocking algorithm for performing approximate clustering
Information Sciences: an International Journal
A search space "cartography" for guiding graph coloring heuristics
Computers and Operations Research
A swarm intelligence approach to the quadratic minimum spanning tree problem
Information Sciences: an International Journal
MTPSO algorithm for solving planar graph coloring problem
Expert Systems with Applications: An International Journal
Further results on swarms solving graph coloring
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Cosine: A new graph coloring algorithm
Operations Research Letters
SWARM GRAPH COLORING FOR THE IDENTIFICATION OF USER GROUPS ON ERP LOGS
Cybernetics and Systems - Intelligent Network Security and Survivability
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This paper gives an extensive empirical evaluation of the innovative nature inspired Gravitational Swarm Intelligence (GSI) algorithm solving the Graph Coloring Problem (GCP). GSI follows the Swarm Intelligence problem solving approach, where the spatial position of agents is interpreted as problem solution and agent motion is determined solely by local information, avoiding any central control system. To apply GSI to search for solutions of GCP, we map agents to graph's nodes. Agents move as particles in the gravitational field defined by goal objects corresponding to colors. When the agents fall in the gravitational well of the color goal, their corresponding nodes are colored by this color. Graph's connectivity is mapped into a repulsive force between agents corresponding to adjacent nodes. We discuss the convergence of the algorithm, testing it over an extensive suite of well-known benchmarking graphs. Comparison of this approach to state-of-the-art approaches in the literature shows improvements in many of the benchmark graphs.