Hybrid ACO and Tabu Search for Large Scale Information Retrieval

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


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.


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


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.USTHBAlgeriaAfrica

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