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When dealing with large scale applications, data sets are huge and very often not obvious to tackle with traditional approaches. In web information retrieval, the greater the number of documents to be searched, the more powerful approach required. In this work, we develop document search processes based on particle swarm optimization and show that they improve the performance of information retrieval in the web context. Two novel PSO algorithms namely PSO1-IR and PSO2-IR are designed for this purpose. Extensive experiments were performed on CACM and RCV1 collections. The achieved results exhibit the superiority of PSO2-IR on all the others in terms of scalability while yielding comparable quality.