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Metasearch engine integrates search results from multiple underlying search engines, improving recall ratio in the big data environment. Multi-agent system is an important way to implement metasearch engine. Great progress has been made in this area, however the previous studies are still short of personalization level. To improve the precision ratio, this paper proposes a personalized metasearch engine which of Agent-based architecture. According to click-through data, the metasearch engine has the ability to schedule the appropriate search engines based on the expertness model, merge all of results into a single list by taking user interest into account, and provide personalized recommendation. Experimental results show that the proposed personalized metasearch engine performs better on precision. It is feasible to provide the required search results more effectively.

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A holonic multi-agent system combines the concept of a holon with a multi-agent system; this combination has been proven to be an effective way to build a complex system. Great progress has been made in this area, but previous studies are fragmented and lack of a task-based perspective to model different systems in the real world. Therefore, this paper proposes a formalistic model for HMAS from a task-based perspective. Not only the static organizational structure is designed, but also the dynamic running mechanism, including the self-adaptive mechanism and the task assignment mechanism based on the proposed holonic structure, are also discussed. Finally, a case study is provided to verify the self-adaptive mechanism. The experimental results show that our proposed DHMAS has the ability to adapt to the changing environment, and performs better in terms of the success rate and the response time when the system is heavily loaded.