An Optimization-Based Aggregation Approach with Triangular Intuitionistic Fuzzy Numbers in High-Dimensional Multi-Attribute Decision-Making

We address information fusion and spatial structure modeling in high-dimensional fuzzy multi-attribute decision-making by proposing a novel framework that couples Triangular Intuitionistic Fuzzy Numbers (TIFNs) with the Plant Growth Simulation Algorithm (PGSA). The method first maps the triangular intuitionistic fuzzy information of experts on each evaluation scheme into high-dimensional spatial points to realize the structured expression of decision-making information. Subsequently, the PGSA is used to perform dynamic global optimization search on the high-dimensional point cloud to determine the optimal set point and realize the intelligent aggregation of heterogeneous fuzzy data from multiple sources. The algorithm breaks through the limitation of traditional linear aggregation on the portrayal of information spatial distribution and is able to improve the accuracy and consistency of decision-making results in high-dimensional complex environments. The experimental results show that the method in this paper outperforms the mainstream aggregation methods in a number of evaluation indexes such as weighted Hamming distance, correlation, information energy and correlation coefficient. The proposed model provides a new technical path for intelligent solution and theory expansion of high-dimensional fuzzy decision-making problems.