A Hybrid Ant Colony Optimization Algorithm for the Far From Most String Problem

  • Christian Blum
  • Paola Festa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8600)

Abstract

The far from most string problem belongs to the family of string selection and comparison problems known as sequence consensus problems, where a finite set of sequences is given and one is interested in finding their consensus, that is, a new sequence that represents as much as possible all the given sequences. Among the consensus problems, the far from most string problem is computationally one of the hardest ones with applications in several fields, including molecular biology where one is interested in creating diagnostic probes for bacterial infections or in discovering potential drug targets.

This paper comes with several contributions. On one side, the first linear integer programming formulation for the considered problem is introduced. On the other side, a hybrid ant colony optimization approach for finding good approximate solution to the problem is proposed. Both approaches are compared to the current state of the art, which is a recently proposed hybrid GRASP with path-relinking. Computational results on a large set of randomly generated test instances indicate that the hybrid ACO is very competitive.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Transactions on Man, Systems and Cybernetics – Part B 34(2), 1161–1172 (2004)CrossRefGoogle Scholar
  2. 2.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefMATHGoogle Scholar
  3. 3.
    Feo, T., Resende, M.: A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. 8, 67–71 (1989)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Ferone, D., Festa, P., Resende, M.: Hybrid metaheuristics for the far from most string problem. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds.) HM 2013. LNCS, vol. 7919, pp. 174–188. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Festa, P.: On some optimization problems in mulecolar biology. Mathematical Bioscience 207(2), 219–234 (2007)CrossRefMATHMathSciNetGoogle Scholar
  6. 6.
    Festa, P., Pardalos, P.: Efficient solutions for the far from most string problem. Annals of Operations Research 196(1), 663–682 (2012)CrossRefMATHMathSciNetGoogle Scholar
  7. 7.
    Festa, P., Resende, M.: GRASP: An annotated bibliography. In: Ribeiro, C., Hansen, P. (eds.) Essays and Surveys on Metaheuristics, pp. 325–367. Kluwer Academic Publishers (2002)Google Scholar
  8. 8.
    Festa, P., Resende, M.: An annotated bibliography of GRASP – Part I: Algorithms. International Transactions in Operational Research 16(1), 1–24 (2009)CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Festa, P., Resende, M.: An annotated bibliography of GRASP – Part II: Applications. International Transactions in Operational Research 16(2), 131–172 (2009)CrossRefMATHMathSciNetGoogle Scholar
  10. 10.
    Glover, F., Laguna, M., Martí, R.: Fundamentals of scatter search and path relinking. Control and Cybernetics 39, 653–684 (2000)Google Scholar
  11. 11.
    Lanctot, J., Li, M., Ma, B., Wang, S., Zhang, L.: Distinguishing string selection problems. Information and Computation 185(1), 41–55 (2003)CrossRefMATHMathSciNetGoogle Scholar
  12. 12.
    Meneses, C., Oliveira, C., Pardalos, P.: Optimization techniques for string selection and comparison problems in genomics. IEEE Engineering in Medicine and Biology Magazine 24(3), 81–87 (2005)CrossRefGoogle Scholar
  13. 13.
    Mousavi, S., Babaie, M., Montazerian, M.: An improved heuristic for the far from most strings problem. Journal of Heuristics 18, 239–262 (2012)CrossRefGoogle Scholar
  14. 14.
    Stützle, T., Hoos, H.H.: \({\cal MAX}\)-\({\cal MIN}\) Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Christian Blum
    • 1
    • 2
  • Paola Festa
    • 3
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of the Basque Country UPV/EHUSan SebastianSpain
  2. 2.IKERBASQUE, Basque Foundation for ScienceBilbaoSpain
  3. 3.Department of Mathematics and Applications ‘‘R. Caccioppoli’’University of Napoli FEDERICO IIItaly

Personalised recommendations