Parametric Design Optimization for Climate-Responsive Façades: A Multi-Objective Genetic Algorithm Approach for Energy Performance and Daylighting in Cairo's High-Rise Residential Buildings

In arid subtropical climates such as Cairo, Egypt (Köppen climate classification BWh), contemporary high-rise residential architecture increasingly relies on extensive glazing envelopes, resulting in elevated solar heat gains, pronounced cooling energy demands, and visual discomfort caused by glare. This study proposes an integrated computational framework combining parametric design with a multi-objective genetic algorithm (NSGA-II) to optimize climate-responsive kinetic and static façade shading systems. Leveraging Grasshopper, Ladybug Tools, and EnergyPlus/Radiance simulation engines, the optimization workflow targets three competing objectives: minimizing total annual cooling energy consumption, maximizing Spatial Daylight Autonomy (sDA_300/50%), and minimizing Annual Sunlight Exposure (ASE_1000/250h) across typical mid- and high-zone residential units. Design variables encompass window-to-wall ratio (WWR), louver tilt angles, perforation ratios, and overhang projection depths evaluated across south, east, and west orientations. The Pareto-optimal frontier generated through 2,500 simulation iterations reveals that optimized self-shading geometries reduce cooling loads by up to 31.4% compared to standard ASHRAE 90.1 baseline envelopes, while achieving an sDA of 78.2% and keeping ASE strictly below the 10% LEED visual discomfort threshold. These findings demonstrate that orientation-specific, algorithmically synthesized shading topologies resolve trade-offs between thermal efficiency and visual ergonomics, providing architects with actionable generative design protocols tailored to dense North African urban contexts.