Hyperheuristic for the Parameter Tuning of a Bio-Inspired Algorithm of Query Routing in P2P Networks

  • Paula Hernández
  • Claudia Gómez
  • Laura Cruz
  • Alberto Ochoa
  • Norberto Castillo
  • Gilberto Rivera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7095)


The computational optimization field defines the parameter tuning problem as the correct selection of the parameter values in order to stabilize the behavior of the algorithms. This paper deals the parameters tuning in dynamic and large-scale conditions for an algorithm that solves the Semantic Query Routing Problem (SQRP) in peer-to-peer networks. In order to solve SQRP, the HH_AdaNAS algorithm is proposed, which is an ant colony algorithm that deals synchronously with two processes. The first process consists in generating a SQRP solution. The second one, on the other hand, has the goal to adjust the Time To Live parameter of each ant, through a hyperheuristic. HH_AdaNAS performs adaptive control through the hyperheuristic considering SQRP local conditions. The experimental results show that HH_AdaNAS, incorporating the techniques of parameters tuning with hyperheuristics, increases its performance by 2.42% compared with the algorithms to solve SQRP found in literature.


Parameter Tuning Hyperheuristic SQRP 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Yang, K., Wu, C., Ho, J.: AntSearch: An ant search algorithm in unstructured peer-to-peer networks. IEICE Transactions on Communications 89(9), 2300–2308 (2006)CrossRefGoogle Scholar
  2. 2.
    Michlmayr, E.: Ant Algorithms for Self-Organization in Social Networks. PhD thesis, Women’s Postgraduate College for Internet Technologies, WIT (2007)Google Scholar
  3. 3.
    Aguirre, M.: Algoritmo de Búsqueda Semántica para Redes P2P Complejas. Master’s thesis, División de Estudio de Posgrado e Investigación (2008)Google Scholar
  4. 4.
    Rivera, G.: Ajuste Adaptativo de un Algoritmo de Enrutamiento de Consultas Semánticas en Redes P2P. Master’s thesis, División de Estudio de Posgrado e Investigación, Instituto Tecnológico de Ciudad Madero (2009)Google Scholar
  5. 5.
    Gómez, C.: Afinación Estática Global de Redes Complejas y Control Dinámico Local de la Función de Tiempo de Vida en el Problema de Direccionamiento de Consultas Semánticas. PhD thesis, Instituto Politécnico Nacional, Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Unidad Altamira (2009)Google Scholar
  6. 6.
    Cruz, L., Gómez, C., Aguirre, M., Schaeffer, S., Turrubiates, T., Ortega, R., Fraire, H.: NAS algorithm for semantic query routing systems in complex networks. In: DCAI. Advances in Soft Computing, vol. 50, pp. 284–292. Springer, Heidelberg (2008)Google Scholar
  7. 7.
    Garrido, P., Riff, M.-C.: Collaboration Between Hyperheuristics to Solve Strip-Packing Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 698–707. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Garrido, P., Castro, C.: Stable Solving of CVRPs Using Hyperheuristics. In: GECCO 2009, Montréal, Québec, Canada, July 8-12 (2009)Google Scholar
  9. 9.
    Han, L., Kendall, G.: Investigation of a Tabu Assisted Hyper-Heuristic Genetic Algorithm. In: Congress on Evolutionary Computation, Canberra, Australia, pp. 2230–2237 (2003)Google Scholar
  10. 10.
    Cowling, P., Kendall, G., Soubeiga, E.: A Hyperheuristic Approach to Scheduling a Sales Summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Özcan, E., Bilgin, B., Korkmaz, E.: A Comprehensive Analysis of Hyper-heuristics. Journal Intelligent Data Analysis. Computer & Communication Sciences 12(1), 3–23 (2008)Google Scholar
  12. 12.
    Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: Exploring Hyper-Heuristic Methodologies With Genetic Programming. In: Mumford, C.L., Jain, L.C. (eds.) Computational Intelligence. ISRL, vol. 1, pp. 177–201. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Eiben, A., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Transactions on Evolutionary Computation 3(2), 124–141 (1999)CrossRefGoogle Scholar
  14. 14.
    Birattari, M.: The Problem of Tuning Metaheuristics as seen from a machine learning perspective. PhD thesis, Universidad libre de Bruxelles (2004)Google Scholar
  15. 15.
    Michalewicz, Z., Fogel, D.: How to Solve It: Modern Heuristics. segunda edición. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  16. 16.
    Gómez, C.G., Cruz, L., Meza, E., Schaeffer, E., Castilla, G.: A Self-Adaptive Ant Colony System for Semantic Query Routing Problem in P2P Networks. Computación y Sistemas 13(4), 433–448 (2010) ISSN 1405-5546 Google Scholar
  17. 17.
    Montresor, A., Meling, H., Babaoglu, Ö.: Towards Adaptive, Resilient and Self-organizing Peer-to-Peer Systems. In: Gregori, E., Cherkasova, L., Cugola, G., Panzieri, F., Picco, G.P. (eds.) NETWORKING 2002. LNCS, vol. 2376, pp. 300–305. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Ardenghi, J., Echaiz, J., Cenci, K., Chuburu, M., Friedrich, G., García, R., Gutierrez, L., De Matteis, L., Caballero, J.P.: Características de Grids vs. Sistemas Peer-to-Peer y su posible Conjunción. In: IX Workshop de Investigadores en Ciencias de la Computación (WICC 2007), pp. 587–590 (2007) ISBN 978-950-763-075-0Google Scholar
  19. 19.
    Halm M., LionShare: Secure P2P Collaboration for Academic Networks. In: EDUCAUSE Annual Conference (2006) Google Scholar
  20. 20.
    Defense Advanced Research Project Agency (2008),
  21. 21.
    Santillán, C.G., Reyes, L.C., Schaeffer, E., Meza, E., Zarate, G.R.: Local Survival Rule for Steer an Adaptive Ant-Colony Algorithm in Complex Systems. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds.) Soft Computing for Recognition Based on Biometrics. SCI, vol. 312, pp. 245–265. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  23. 23.
    García, S., Molina, D., Lozano, F., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC 2005 Special Session on Real ParameterOptimization. Journal of Heuristics (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Paula Hernández
    • 1
  • Claudia Gómez
    • 1
  • Laura Cruz
    • 1
  • Alberto Ochoa
    • 2
  • Norberto Castillo
    • 1
  • Gilberto Rivera
    • 1
  1. 1.División de Estudios de Posgrado e InvestigaciónInstituto Tecnológico de Ciudad Madero.Cd. Madero, TamaulipasMéxico
  2. 2.Instituto de Ingeniería y TecnologíaUniversidad Autónoma de Ciudad Juárez.Cd. Juárez, ChihuahuaMéxico

Personalised recommendations