图书馆借阅信息优化管理仿真研究

Library Information Management Simulation Research

对大规模定制环境下图书馆借阅信息进行管理,可以提升图书馆的信息服务质量。对图书借阅信息分类决定信息管理效果的优劣,进行信息分类时,由于大规模定制环境下的图书馆借阅人群的不同,使得借阅信息不统一,传统的信息分类方法,主要通过对借阅人群进行分析的基础上对借阅信息进行分类,导致分类误差大的问题。提出基于改进人工鱼群的大规模定制环境下图书借阅信息分类方法,对图书馆借阅信息进行优化管理。上述方法先提取不同类型图书信息的特征项,并计算其特征权重,将借阅信息映射成由特征项和权重组成的向量,利用向量夹角余弦计算借阅信息间的相似度,融合于SVM理论组建图书借阅信息分类问题模型,利用人工鱼群算法对模型进行优化求解,依据最优解的图书借阅信息分类模型对大规模定制环境下图书借阅信息进行分类,完成图书馆借阅信息优化管理。仿真结果表明,所提方法可以有效优化图书馆借阅信息管理。

It can improve the information service quality of library to manage the library borrowing information under large-scale customization environment. The borrow information classification determines the advantage and disadvantage of information management effect. The borrowing crowd of library is different under large-scale customization environment during information classification. It makes the borrowing information disunity. Traditional information classification method classifies the borrowing information based on analyzing the borrowing crowd. It leads to big clas-sification error. In this paper, we propose an information classification method of book borrowing under large-scale customization environment based on the modified artificial fish swarm. Firstly, the method extracts the feature item of different type of book information and calculates the feature weight to map the borrowing information as the vector consisting of feature item and weight. The included angle cosine of vector is used to calculate the similarity among borrowing information. Then, it builds the classification problem model of book borrowing information integrated with the SVM theory. Finally, it uses artificial fish swarm algorithm to make optimization solution and classifies the book borrowing information under large-scale customization environment according to the book borrowing information with optimization solution to complete the optimization management of borrowing information. The simulation results show that the method mentioned above can optimize the information management.