Graph Partitioning Using Improved Ant Clustering

  • M. Sami Soliman
  • Guanzheng Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)


Parallel computing, network partitioning, and VLSI circuit placement are fundamental challenges in computer science. These problems can be modeled as graph partitioning problems. A new Similarity carrying Ant Model (SCAM) is used in the ant-based clustering algorithm to solve graph partitioning problem. In the proposed model, the ant will be able to collect similar items while it moves around. The flexible template mechanism had been used integrated with the proposed model to obtain the partitioning constrains. Random graph has been used to compare the new model with the original ant model and the model with short-term memory. The result of the experiments proves the impact of the SCAM compared with other models. This performance improvement for ant clustering algorithm makes it is feasible to be used in graph portioning problem.


graph portioning ant-based clustering similarity template 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Teresco, J.D., Faik, J., Flaherty, J.E.: Hierarchical Partitioning and Dynamic Load Balancing for Scientific Computation. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds.) PARA 2004. LNCS, vol. 3732, pp. 911–920. Springer, Heidelberg (2006)Google Scholar
  2. 2.
    Boman, E.G., Catalyurek, U.V., Chevalier, C., Devine, K.D., Safro, I., Wolf, M.M.: Advances in Parallel Partitioning, Load Balancing and Matrix Ordering for Scientific Computing. Journal of Physics: Conference Series 180(1), 12008–12013 (2009)CrossRefGoogle Scholar
  3. 3.
    Kumar, S., Das, S.K.: Graph Partitioning for Parallel Applications in Heterogeneous Grid Environments. In: 16th International Parallel and Distributed Processing Symposium, IPDPS 2002, Lauderdale, Florida, USA, pp. 66–71 (2002)Google Scholar
  4. 4.
    Schaeffer, S.E.: Graph Clustering. Computer Science Review 1(1), 27–64 (2007)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Bui, T.N., Jones, C.: Finding Good Approximate Vertex and Edge Partitions Is NP-Hard. Information Processing Letters 42, 153–159 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Peiravi, A., Ildarabadi, R.: Complexities of Using Graph Partitioning in Modern Scientific Problems and Application to Power System Islanding. Journal of American Science 5(5), 1–12 (2009)Google Scholar
  7. 7.
    Bonabeau, E., Dorigo, M., Theraulax, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  8. 8.
    Garey, M.R., Johnson, D.S., Stockmeyer, L.: Some Simplified NP-Complete Graph Problems. Theoretical Computer Science 1, 237–267 (1976)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: The first international conference on simulation of adaptive behavior on from animals to animats, pp. 356–363. MIT Press, Cambridge (1990)Google Scholar
  10. 10.
    Lumer, E., Faieta, B.: Diversity and Adaptation in Populations of Clustering Ants. In: Proceedings of Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats, vol. 3, pp. 499–508. MIT Press, Cambridge (1994)Google Scholar
  11. 11.
    Kuntz, P., Layzell, P., Snyers, D.: A Colony of Ant-Like Agents for Partitioning in VLSI Technology. In: Husbands, P., Harvey, I. (eds.) Fourth European Conference on Artificial Life, pp. 417–424. MIT Press, Cambridge (1997)Google Scholar
  12. 12.
    Handl, J., Knowles, J., Dorigo, M.: Strategies for the increased robustness of ant-based clustering. In: Di Marzo Serugendo, G., Karageorgos, A., Rana, O.F., Zambonelli, F. (eds.) ESOA 2003. LNCS (LNAI), vol. 2977, pp. 90–104. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Ong, S.L., Lai, W.K., Tai, T.S.Y., Hoe, K.M.: Application of ant-based template matching for web documents categorization. Informatica 29, 173–181 (2005)Google Scholar
  14. 14.
    Garbers, J., Promel, H.J., Steger, A.: Finding Clusters in VLSI Circuits. In: IEEE International Conference on Computer-Aided Design, pp. 520–523. IEEE Computer Society Press, Los Alamitos (1990)Google Scholar
  15. 15.
    Peterson, G.L., Mayer, C.B.: Ant clustering with locally weighted ant perception and diversified memory. Swarm Intelligence 2(1), 43–68 (2008)CrossRefGoogle Scholar
  16. 16.
    Handl, J.: Ant-based methods for tasks of clustering and topographic mapping: extensions, analysis and comparison with alternative methods. Masters Thesis, Chair of Artificial Intelligence, University of Erlangen-Nuremberg, Germany (2003)Google Scholar
  17. 17.
    Santos, J.M., Embrechts, M.: On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds.) ICANN 2009. LNCS, vol. 5769, pp. 175–184. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • M. Sami Soliman
    • 1
  • Guanzheng Tan
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaP.R. China

Personalised recommendations