Social network group decision-making with linguistic Z-number preference relations based on personalized individual semantics and trust driven

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-05-01 DOI:10.1016/j.asoc.2025.113166

Xiao-Yun Lu , He-Cheng Li , Ze-Hui Chen

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引用次数: 0

Abstract

Compared to preference relations (PRs) based on one-dimensional data description, linguistic Z-number (LZN) preference relations (LZPRs) exhibit more advantages in expressing uncertainty information when comparing objectives. However, the extant preference group decision-making (PGDM) with LZPRs focus on traditional group decision-making (TGDM) problems. In addition, there are certain shortcomings in the consistency analysis of LZPRs proposed in the PGDM with LZPRs. Therefore, this study will focus on discussing the PGDM with LZPRs based on dynamic social networks. Firstly, A new additively consistent concept for LZPRs is presented, and the social network structure based on LZN trust relationships is constructed. Furthermore, a synthetical personalized individual semantics (PIS) determination method based on consistency driven and social network driven is developed for the credibility of evaluation values in LZPRs. Secondly, a dynamic mixed experts weights determination method based on experts’ opinions and social network trust relationships between experts has been proposed, considering multiple indicators of experts’ opinions. Thirdly, a synthetical consensus improving algorithm based on dynamic trust-based feedback adjustment mechanism is designed by integrating optimization-based consensus strategy and identification rule (IR) and direction rule (DR) strategy. Finally, the rationality and effectiveness of the proposed method are verified through a numerical example. Meanwhile its merits are illustrated by comparison analyses.

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基于个性化个体语义和信任驱动的语言z数偏好关系的社会网络群体决策

相对于基于一维数据描述的偏好关系，语言z数偏好关系在表达目标比较中的不确定性信息方面更有优势。然而，现有的基于lzpr的偏好群体决策（PGDM）主要关注传统群体决策（TGDM）问题。此外，PGDM中提出的lzpr与lzpr的一致性分析也存在一定的不足。因此，本研究将重点讨论基于动态社交网络的lzpr与PGDM。首先，提出了lzpr的加性一致性概念，构建了基于lzpr信任关系的社会网络结构。在此基础上，提出了一种基于一致性驱动和社会网络驱动的综合个性化个体语义（PIS）确定方法。其次，考虑专家意见的多个指标，提出了一种基于专家意见和专家之间的社会网络信任关系的动态混合专家权重确定方法；第三，将基于优化的共识策略与识别规则（IR）和方向规则（DR）策略相结合，设计了一种基于动态信任反馈调节机制的综合共识改进算法。最后通过一个算例验证了所提方法的合理性和有效性。同时通过对比分析说明了其优点。

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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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.