Optimized design solutions for multifamily housing: A multi-objective approach to thermal resilience under heatwave conditions

Heatwaves are becoming more frequent and severe under climate change, intensifying thermal stress in residential buildings, particularly in hot and humid regions. This study applies a multi-objective optimization framework, based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II), to evaluate and enhance the thermal resilience of a representative multifamily housing typology in São Paulo, Brazil, under historical and projected future heatwave conditions. The optimization addressed the three stages of resilience—resistance, robustness, and recovery—by minimizing cooling load and maximum operative temperature, while constraining recovery time. Results were contrasted with a parametric simulation approach relying on predefined passive cooling strategies. The NSGA-II framework consistently achieved superior performance across all resilience indicators. Cooling load was reduced by up to 65 %, maximum operative temperature was maintained near 35 °C (while parametric simulation models exceeded 42 °C), and recovery time remained below 384 h. In contrast, parametric simulation cases showed significant trade-offs, with extended recovery periods exceeding 1000 h and greater variability across indicators. Robust design features emerging from optimized solutions included low façade absorptance, high roof thermal mass with added insulation, solar-control glazing, and open balconies. By integrating MOO with a resilience-based performance framework, this study captures the multi-stage dynamics of thermal resilience more comprehensively. This integrative approach moves beyond conventional building simulation assessments and offers a replicable method to support climate-adaptive design for heat-vulnerable buildings.