Abstract: In order to improve the efficiency of database selection, and solve problem of the multi-attribute group decision making (MAGDM). It puts forward a method of the database selection based on hesitant linguistic information aggregation. Firstly, a database selection model on the basis of the generalized hesitant linguistic Heronian mean (GHLHM) operator is developed. Secondly, under the hesitant linguistic environment, the new operations are defined based on the Archimedean norms. Then, by using the defined operational laws and Heronian mean, the GHLHM operator is proposed, whose desirable properties and some special cases are discussed in detail. The generalized hesitant linguistic weighted Heronian mean (GHLWHM) operator is introduced. In addition, a novel hesitant fuzzy linguistic MAGDM model based on GHLWHM operator is investigated, which can capture the relationship among the input decision making information and enable decision maker to select different parameters to make decision. Finally, the practicality and effectiveness of the developed model is illustrated with a numerical example for the selection of database. Experiments show that this method can achieve comprehensive optimization of database performance, and has broad application prospects in other fields.

Key words: Multi-attribute group decision making, hesitant fuzzy linguistic set, Heronian mean, Archimedean norm, information aggregation operator, database

摘要： 为了提高数据库选择效率，针对多属性群决策问题提出了一种基于犹豫语言多属性群决策算法的数据库选择方法。首先，构建一种基于广义犹豫语言Heronian平均(GHLHM)算子的数据库选择模型。其次，将Archimedean范数引入到犹豫语言环境下，定义了新的犹豫语言运算法则；其次，基于新的运算法则和Heronian平均，提出了GHLHM算子，并探讨了GHLHM算子的一些基本性质，研究了GHLHM算子的几类常用算子形式，提出了广义犹豫语言加权Heronian平均(GHLWHM)算子；最后，基于GHLWHM算子构建了一种新的犹豫语言多属性群决策方法并将其应用于数据库选择中，实验证明，该方法可
以实现对数据库性能的综合优选排序，在其他领域也有着广泛的应用前景。

关键词: 多属性群决策, 犹豫模糊语言集, Heronian平均, Archimedean范数, 信息集成算子, 数据库