利用粒子群算法优化多源检索融合结果的方法

Optimizing Merging Results of Multiple Resource Retrievals by a Particle Swarm Algorithm

对多个搜索引擎系统返回结果进行自动整合,是当前网络信息检索应用至今尚未较好解决的一个难点,也是影响元搜索引擎效果的关键技术环节.在实验多种处理多源搜索结果融合算法的基础上,文中提出一种可对多种其它融合排序算法输出结果做进一步优化的离散粒子群算法.该算法不仅能在整体效果上优于作为其预处理输入的其它融合排序算法,而且对不同查询有更好的适应性,不需考虑各独立源检索返回结果的质量权重及相互间重叠率等因素.与作为其输入处理的其它融合算法相比,该算法的相关文档识别准确率可提高约20%,而准确率随查询主题变化的标准差可降低约50%.

To automatically merge the result from multiple independent research engines （IREs） is a key component of the metasearch engine development and it is problem in distributed information retrieval applications as well. After testing a variety of existing result merging algorithms for multiple IRE results, a discrete particle swarm algorithm （DPSA） is proposed for further optimizing a group of merging results produced by other result merging algorithms. The experimental results show that the DPSA generally outperforms all the other result merging algorithms. It usually has better adaptability in application for not having to take into account the usefulness weights of IRE results and the overlap rate among different IRE results of a query. Compared to other result merging algorithms, the recognition precision of DPSA increases nearly 20%, while the precision standard deviation for different queries decreases about 50%.