基于熵量的 TOPSIS 法解决球形模糊软信息的多属性群体决策问题

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Computational Intelligence and Soft Computing Pub Date : 2023-11-18 DOI:10.1155/2023/7927541

Perveen P. A. Fathima, Sunil Jacob John, T. Baiju

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引用次数: 0

摘要

球形模糊软集（SFSS）是一种广义的软集模型，它更加合理、实用和精确。作为一种非常自然的泛化，引入 SFSS 的不确定性度量似乎非常重要。本文定义了 SFSS 的熵、相似性和距离度量的概念，并提出了球形模糊软熵的特征。此外，还详细讨论了熵和相似性度量以及熵和距离度量之间的关系。作为应用，提出了一种基于改进的理想解相似度排序偏好技术（TOPSIS）和拟议的 SFSS 熵度量的算法，以解决多属性群体决策问题。最后，通过一个示例证明了所推荐算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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TOPSIS Method Based on Entropy Measure for Solving Multiple-Attribute Group Decision-Making Problems with Spherical Fuzzy Soft Information

A spherical fuzzy soft set (SFSS) is a generalized soft set model, which is more sensible, practical, and exact. Being a very natural generalization, introducing uncertainty measures of SFSSs seems to be very important. In this paper, the concept of entropy, similarity, and distance measures are defined for the SFSSs and also, a characterization of spherical fuzzy soft entropy is proposed. Further, the relationship between entropy and similarity measures as well as entropy and distance measures are discussed in detail. As an application, an algorithm is proposed based on the improved technique for order preference by similarity to an ideal solution (TOPSIS) and the proposed entropy measure of SFSSs, to solve the multiple attribute group decision-making problems. Finally, an illustrative example is used to prove the effectiveness of the recommended algorithm.

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来源期刊

Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

6.10

自引率

3.40%

发文量

审稿时长

21 weeks

期刊介绍： Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.