基于优化策略的三角直觉模糊信息融合模型及其在高维多属性决策中的应用

A Triangular Intuitionistic Fuzzy Information Fusion Model Based on Optimization Strategies and Its Application in High-Dimensional Multi-Attribute Decision Making

摘要: 为应对高维模糊决策中信息融合与空间建模的难题，本文提出了一种将三角直觉模糊数(TIFNs)与植物生长模拟算法(PGSA)相结合的新型方法。首先，将专家评价映射为高维空间点，实现模糊信息的结构化表示；随后，PGSA在点云中执行动态全局搜索，以确定最优聚合点，从而实现异构模糊数据的智能融合。实验结果表明，在加权汉明距离、相关性、信息能量及相关系数等多个评价指标上，该方法均优于主流方法。研究为高维模糊决策的智能化求解与理论拓展提供了一条新的技术路径。本文所述“高维”主要指属性维度 ≥ 5的多属性决策问题，文中实验以五维空间建模为例进行验证。

Abstract: To address the challenges of information fusion and spatial modeling in high-dimensional fuzzy decision-making, this paper proposes a novel method that combines Triangular Intuitionistic Fuzzy Numbers (TIFNs) with the Plant Growth Simulation Algorithm (PGSA). First, expert evaluations are mapped to points in high-dimensional space, achieving a structured representation of fuzzy information. Subsequently, PGSA performs dynamic global search in the point cloud to determine the optimal aggregation point, thereby enabling intelligent fusion of heterogeneous fuzzy data. Experimental results show that this method outperforms mainstream approaches across multiple evaluation metrics, including weighted Hamming distance, correlation, information energy, and correlation coefficient. This research provides a new technical path for intelligent solutions and theoretical expansion in high-dimensional fuzzy decision-making. The term “high-dimensional” in this paper primarily refers to multi-attribute decision-making problems with attribute dimensions ≥ 5, and the experiments in this study are verified using five-dimensional space modeling as an example.