Liyuan Li, Changzhi Zhang, Chuang Niu, Hao Zhang
This study introduces a parametric multi-objective optimization framework for urban block morphology. It integrates micro-climate data corrected by the Urban Weather Generator (UWG), energy simulation through EnergyPlus and Honeybee, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) within the Wallacei platform. Using Wuhan, China, a city with a representative hot-summer and cold-winter climate, as a case study, the framework simultaneously optimizes three key objectives: Average Sunshine Hours (Av.SH), Energy Use Intensity (EUI), and Average Universal Thermal Climate Index (Av.UTCI). The framework systematically links parametric modeling, environmental simulation, and evolutionary optimization to explore how block typologies and height configurations affect the trade-offs among solar access, energy demand, and outdoor thermal comfort. Among the feasible solutions, Av.SH exhibits the greatest variation, ranging from 4.30 to 7.93 h, followed by Av.UTCI (44.13 to 45.46 °C), while EUI shows the least fluctuation, from 91.69 to 93.36 kWh/m2. Key design variables, such as building type and height distribution, critically influence the outcomes. Optimal configurations are achieved by interweaving low-rise (2 to 3 floors), mid-rise (6 to 8 floors), and high-rise (15 to 20 floors) buildings to enhance openness and ventilation. The proposed framework offers a quantifiable strategy for guiding future climate-responsive and energy-efficient neighborhood design.