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Hybrid ACO and Tabu Search for Large Scale Information Retrieval

  • Yassine DriasEmail author
  • Samir Kechid
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 607)

Abstract

This paper presents an attempt to tackle information retrieval (IR) with meta-heuristics. For this aim, we propose two ACO algorithms for information retrieval on large-scale data sets. The main hard issue of this study resides in modeling information retrieval using meta-heuristics that often necessitate links between documents in order to realize move operations from one document to another during the search process. The first novelty in this work is the design of such model to adapt ACO approaches and even other meta-heuristics to IR. The second one resides in the hybridization of ACO approaches with tabu search in order to achieve more efficiency. The designed algorithms and a classical information retrieval method were implemented for comparison purposes. Experiments were conducted on CACM, RCV1 and random benchmarks. Numerical results show that ACO is scalable while achieving the same performance as the traditional IR process in terms of solutions quality.

Keywords

Information retrieval Large-scale data sets Hybrid meta-heuristics ACO Tabu search CACM RCV1 

References

  1. 1.
    Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life. 5–3, 137–172 (1999)Google Scholar
  2. 2.
    Van Rijsbergen C.J.: Information Retrieval. Information Retrieval Group University of Glasgow, Glasgow (1979)Google Scholar
  3. 3.
    Bulheimer, B., Hartl, R.F., Strauss, C.: A new rank based version of the ant system, a computational study. Technical Report POM -03/97, Institute of Management Science, University of Vienna (1997)Google Scholar
  4. 4.
    Cordon, O., Deviana, I., Herrera, F., Moreno, L.: A new ACO model integrating evolutionary computation concepts: the best-worst ant system. In: From Ant Colonies to Artificial Ants, ANTS 2000, pp. 22–29 (2000)Google Scholar
  5. 5.
    Dorigo, M., Gambardella, L.M.: Ant algorithms for the traveling salesman problem. Biosystems 43, 73–81 (1997)CrossRefGoogle Scholar
  6. 6.
    Hsinchun, C.: Machine learning for information retrieval: neural networks, symbolic learning and genetic algorithms. J. Am. Soc. Inf. Sci. 46, 194–216 (1995)CrossRefGoogle Scholar
  7. 7.
    Doerner, K., Hartl, R.F., Reimann, M.: Cooperative ant colonies for optimizing resource allocation in transportation. LNCS Springer Verlag 2037, 70–79 (2001)Google Scholar
  8. 8.
    Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant system for job-shop scheduling. Belgian J. Oper. Res. Stat. Comput. Sci. 34–1, 39–53 (1994)Google Scholar
  9. 9.
    Gambardella, L.M., Taillard, E., Dorigo, M.: Ant algorithms for the QAP. Technical Report IDSIA 97–4. Lugano, Switzerland (1997)Google Scholar
  10. 10.
    Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)Google Scholar
  11. 11.
    Pathak, P., Gordon, M., Fan, W.: Effective Information retrieval using genetic algorithms based matching functions adaptation. In: 33rd IEEE HICSS (2000)Google Scholar
  12. 12.
    Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Inf. Process. Manage. 24, 513–523 (1988)Google Scholar
  13. 13.
    Stutzle, T., Hoos, H.: Improving the ant system: a detailed report on the MAX-MIN ant system. In: ICANGA, pp. 245–249. Springer (1997)Google Scholar
  14. 14.
    Baeza-Yates, R., Ribiero-Neto, B.: Modern Information Retrieval. Wesley Longman Publishing Co., Inc., Boston (1999)Google Scholar
  15. 15.
    Mahdavi, M., Chehreghani, M.H., Abolhassani, H., Forsati R.: Novel meta-heuristic algorithms for clustering web documents. Appl. Math. Comput. 201, 441–451 (2008)Google Scholar
  16. 16.
    Zhengyu, Z., Xinghuan, C., Qingsheng, Z., Qihong, X.: A GA-based query optimization method for web information retrieval. Appl. Math. Comput. 185, 919–930 (2007)CrossRefzbMATHGoogle Scholar
  17. 17.
    Lesk, M.E., Schmidt, E.: Lex—A lexical analyzer generator. UNIX time-sharing system: UNIX Programmer’s Manual, 7th edn, vol. 2B (1975)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.USTHBAlgeriaAfrica

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