针对传统flooding搜索算法面临的效率低下和网络流量过大等问题,提出了一种改进的基于兴趣和蚁群相结合的资源搜索算法( IASA).该算法将TrL机制加以改进,并将兴趣相似度引入到蚁群算法的路径概率选择中,同时结合信息素的多样性和正反馈机制,积累历史搜索经验,获得路由指引信息,从而有效地指导查询请求消息的转发,将请求消息尽量发往资源可能存在的节点上.仿真实验表明:该算法能有效地指导资源搜索的方向,提高查询命中率,减少冗余消息包,其整体搜索效果较好.%In unstructured P2P network, the traditional flooding search algorithm suffers from some disadvantages of inefficiencies and excessive network traffic. To address the problem .present a resource search algorithm based on the combination of interest and ant colony (IAS A). In this algorithm, the TTL mechanism was improved and interest similarity was introduced into the choice of the rooting path in ant colony algorithm .and combining with the diversity of the pheromones, positive feedback mechanism was conductive to accumulate history experience and get routing guiding information,so as to effectively guide query information's forwarding,so that the query information can be sent to the proper nodes with requested resources as much as possible. Simulation results indicated that the algorithm can effectively guide the search direction and improve inquires and reduce redundancy information. On the whole, the search effect performs better.
展开▼
