Numerical Aggregation and Evaluation of High-Dimensional Multi-Expert Decisions Based on Triangular Intuitionistic Fuzzy Modeling

To address the challenges of high-dimensional complexity and increasing heterogeneity in expert opinions, this study proposes a novel numerical aggregation model for multi-expert decision making based on triangular intuitionistic fuzzy numbers (TIFNs) and the Plant Growth Simulation Algorithm (PGSA). The proposed framework transforms experts’ fuzzy preference information into five-dimensional geometric vectors and employs the PGSA to perform global optimization, thereby yielding an optimized collective decision matrix. To comprehensively evaluate the aggregation performance, several quantitative indicators—such as weighted Hamming distance, correlation sum, information intuition energy, and weighted correlation coefficient—are introduced to assess the results from the perspectives of consensus, stability, and informational strength. Extensive numerical experiments and comparative analyses demonstrate that the proposed method significantly improves expert consensus reliability and aggregation robustness, achieving higher decision accuracy than conventional approaches. This framework provides a scalable and generalizable tool for high-dimensional fuzzy group decision making, offering promising potential for complex real-world applications.