Multi-Objective Optimization of Residential Layout and Envelope Design for Daylight, Thermal Comfort, and Cooling Energy in Hot-Arid Climates

Early-stage architectural design decisions significantly influence the long-term energy use, daylight performance, and indoor environmental quality of residential buildings. However, the complexity and computational cost of simulations often prevent comprehensive exploration, leading to suboptimal building design in hot-arid regions where cooling demands dominate. This paper proposes an integrated computational framework that enables the co-evolutionary optimization of residential building layouts and envelope parameters. Set in the extreme climate of United Arab Emirates, the workflow combines a custom Grasshopper parametric script with high-fidelity Radiance daylight modeling and EnergyPlus thermal and energy simulations. A multi-objective genetic algorithm (NSGA-II via Wallacei X) is deployed to evaluate 2,592 design variations related to envelope (building orientation, window-to-wall ratio, and glazing type) across three common residential typologies, utilizing a weighted objective function prioritizing cooling Energy Use Intensity, then thermal comfort (Overheating Hours), and last Useful Daylight Intensity. The optimization successfully bounded the average cooling EUI around 87 kWh/m2, constrained overheating to under 16%, and settled useful daylight near 43% across all configurations. The results show that a square with courtyard layout provides optimal balance for thermal and daylight performance while achieving an optimum EUI of 82.7 kWh/m2. Following the analysis, actionable design guidelines include a minimized 20% WWR across exposed facades and a low U-value and SHGC glazing as regional necessities. Ultimately, this framework provides architects with a data-driven, replicable methodology to comprehensively explore early design options, facilitating the realization of sustainable high-performing housing in hot-arid environments.