Finding the Best Parameter Setting Particle Swarm Optimisation

Information Retrieval techniques traditionally depend on the setting of one or more parameters. Depending on the problem and the techniques the number of parameters can be one, two or even dozens of them. One crucial problem in Information Retrieval research is to achieve a good parameter setting of its methods. The tuning process, when dealing with several parameters, is a time consuming and critical step. In this paper we introduce the use of Particle Swarm Optimisation for the automatic tuning process of the parameters of Information Retrieval methods. We compare our proposal with the Line Search method, previously adopted in Information Retrieval. The comparison shows that our approach is faster and achieves better results than Line Search. Furthermore, Particle Swarm Optimisation algorithms are suitable for parallelisation, improving the algorithm behaviour in terms of time convergence.