Multiagent Evolutionary Algorithm for T-coloring Problem

  • Jing Liu
  • Weicai Zhong
  • Jinshu Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


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.


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  1. 1.
    Costa, D.: On the use of some known methods for T-colorings of graphs. Annals of Operations Research 41, 343–358 (1993)CrossRefzbMATHGoogle Scholar
  2. 2.
    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)Google Scholar
  3. 3.
    Riihijärvi, J., Petrova, M., Mähönen, P.: Frequency allocation for WLANs using graph coloring techniques. In: WONS, pp. 216–222 (2005)Google Scholar
  4. 4.
    Hurley, S., Smith, D.H.: Bounds for the frequency assignment problem. Discrete Mathematics (167-168), 571–582 (1997)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Russell, S.J., Norvig, P.: A modern approach. Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs (1995)zbMATHGoogle Scholar
  7. 7.
    Hale, W.K.: Frequency Assignment: Theory and Applications. IEEE Transactions on Vehicular Technology 68(12), 1497–1514 (1980)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jing Liu
    • 1
  • Weicai Zhong
    • 1
  • Jinshu Li
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian University Email: neouma@163.comXi’anChina

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