Web Information Retrieval Using Particle Swarm Optimization Based Approaches
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Bees Swarm Optimization for Real Time Ontology Based Information Retrieval
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Bees Swarm Optimization for Web Association Rule Mining
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Query paraphrasing enhancement using artificial bee colony
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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This paper deals with large scale information retrieval aiming at contributing to web searching. The collections of documents considered are huge and not obvious to tackle with classical approaches. The greater the number of documents belonging to the collection, the more powerful approach required. A Bees Swarm Optimization algorithm called BSO-IR is designed to explore the prohibitive number of documents to find the information needed by the user. Extensive experiments were performed on CACM and RCV1 collections and more large corpuses in order to show the benefit gained from using such approach instead of the classic one. Performances in terms of solutions quality and runtime are compared between BSO and exact algorithms. Numerical results exhibit the superiority of BSO-IR on previous works in terms of scalability while yielding comparable quality.