Application Research of Intelligent Search System Based on Clustering Algorithm

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Tao Liu, Lihong Hao, Saixiao Huang, Xiaoya Qin


In this paper, a multi-agent oriented personalized information retrieval method is proposed. The system is composed of 5 agents and 2 other components. The design highlights the personalization and real-time of the information obtained by users, and has the characteristics of intelligent retrieval and active retrieval. Then this paper studies it from three aspects: user model, system learning model and individual information model. Then a grouping gravity search algorithm is proposed. Compared with conventional gravity search, group gravity search is carried out for a specific decoding strategy. This algorithm is a new gravity search algorithm suitable for packet coding, which makes the packet search process approximate to the iterative optimization of classical gravity algorithm. Finally, the effectiveness of the proposed method is verified on various typical experimental cases. Experiments show that the proposed algorithm has higher classification performance than other intelligent group algorithms.

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