摘要
为了提升分散评估网络信息资源精确搜索质量,需要进行资源搜索方法的研究。但是采用当前方法在进行资源搜索时,没有考虑网络节点价值对资源搜索的影响,存在信息资源搜索误差大的问题。为解决上述问题,提出一种基于混合蚁群的分散评估网络信息资源精确搜索方法。上述方法先利用泛洪方法搜索本地缓存表,如果搜索成功,返回资源地址信息反之,返回搜索失败信息,并促使源节点将返回的搜索结果写入本地缓存表,得到搜索信息资源的路径信息。并用兴趣域分别映射当前搜索请求和节点历史搜索的兴趣类型,将蚁群理论和节点的兴趣因子相融合,动态调整节点价值和信息素,计算转发概率时的权重关系,并提升了对分散评估网络信息资源搜索的质量。仿真证明,所提方法信息搜索效率较高,充分的提升了分散评估网络信息资源搜索的质量。
This paper presents an accurate search method for information resources of decentralized evaluation network based on hybrid ant colony. First of all, this method searches local cache table by using flooding method, if the search is successful, returns resource address information, otherwise, returns search failure information, then urges source node to write returned search results to local cache table and obtains path information about searching information resources, and then respectively maps interest type of current search request and node history search by using interest domain. At last, this method fuses ant colony theory and node interest factor, dynamically adjusts node value and pheromone and calculates weight relation in forwarding probability, improves quality of information resource search of decentralized evaluation network. The simulation results show that this method has high search efficiency and improves quality of information resource search of decentralized evaluation network.
出处
《计算机仿真》
北大核心
2017年第12期398-401,共4页
Computer Simulation
关键词
分散评估网络
信息资源
搜索
decentralized evaluation network
information resources
search
