Multi-Objective Optimization of Building Performance for University Dormitories in Cold Climate Regions During Winter

University dormitories in cold climate regions face the dual challenges of high heating energy consumption and poor outdoor pedestrian comfort during winter. Existing studies on university dormitories have primarily focused on individual building performance optimization, while insufficient attention has been paid to the optimization of dormitory cluster layouts and their multi-objective performance. To address this gap, this study establishes a parametric multi-objective optimization framework to simultaneously minimize building energy use intensity, minimize wind speed at pedestrian height, and maximize outdoor thermal comfort. Based on three floor area ratio scenarios, 24 dormitory prototypes are extracted from three building typologies: row-type buildings, detached buildings, and enclosed buildings. The optimization process was implemented on the Grasshopper platform using the NSGA-II algorithm. Cluster analysis is conducted on the Pareto front, and Pearson correlation analysis is applied to investigate the relationships between six urban morphological parameters and the three optimization objectives. The results indicate that: (1) enclosed buildings (E-1 type) and detached buildings (D-1 type) dominate the Pareto-optimal solution set; (2) high-FAR buildings are predominantly distributed in the northeastern part of the site, while public spaces are concentrated in the central-southern area; (3) correlation analysis indicates that shape coefficient (SC) exhibits the strongest correlations with the three objectives; and (4) compared with dominated solutions, Pareto-optimal solutions reduce WS by 10.46% and EUI by 6.57%, while improving UTCI by 0.05 °C. This study provides quantitative decision-making support for efficient planning and low-carbon design of university dormitory clusters in cold climate regions.