Abstract
With the properties of T-coloring problems in mind, multiagent systems and evolutionary algorithms are integrated to form a new algorithm, Multiagent Evolutionary Algorithm for T-coloring (MAEA-T-coloring). We studied the generalization of classical graph coloring model, and focused our interest in the restricted T-coloring. An agent in MAEA-T-coloring represents a candidate solution to T-colorings. All agents live in a latticelike environment, with each agent fixed on a lattice-point. In order to increase energies, they compete or cooperate with their neighbors using their knowledge. Experiments on large random instances of T-colorings show encouraging results about MAEA- T-coloring.
This work was supported by the National Natural Science Foundations of China under Grant 60502043, 60872135, and 60602064, the Program for New Century Excellent Talents in University of China under Grant NCET-06-0857, the National High Technology Research and Development Program (“863” program) of China under Grant 2006AA01Z107, and the Natural Science Research Project of Shaanxi, China.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Costa, D.: On the use of some known methods for T-colorings of graphs. Annals of Operations Research 41, 343–358 (1993)
Dorne, R., Hao, J.-K.: Tabu search for graph coloring, T-colorings and set T-colorings. In: Meta-heuristics 1998, Theory and Applications, pp. 33–47. Kluwer Academic Publishers, Boston (1998)
Riihijärvi, J., Petrova, M., Mähönen, P.: Frequency allocation for WLANs using graph coloring techniques. In: WONS, pp. 216–222 (2005)
Hurley, S., Smith, D.H.: Bounds for the frequency assignment problem. Discrete Mathematics (167-168), 571–582 (1997)
Janczewski, R., Kubale, M., et al.: The T-DSATUR algorithm: An interesting generalization of the DSATUR algorithm. In: International conference on advanced computer systems (5), pp. 288–292 (1998)
Russell, S.J., Norvig, P.: A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs (1995)
Hale, W.K.: Frequency Assignment: Theory and Applications. IEEE Transactions on Vehicular Technology 68(12), 1497–1514 (1980)
Liu, J., Zhong, W., Jiao, L.: A multiagent evolutionary algorithm for constraint satisfaction problems. IEEE Trans. Syst., Man, and Cybern. B 36(1), 54–73 (2006)
Zhong, W., Liu, J., Xue, M., Jiao, L.: A multiagent genetic algorithm for global numerical optimization. IEEE Trans. Syst., Man, and Cybern. B 34(2), 1128–1141 (2004)
Liu, J., Zhong, W., Jiao, L.: Job-Shop Scheduling Based on Multiagent Evolutionary Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 925–933. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Zhong, W., Li, J. (2008). Multiagent Evolutionary Algorithm for T-coloring Problem. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-89694-4_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89693-7
Online ISBN: 978-3-540-89694-4
eBook Packages: Computer ScienceComputer Science (R0)