Multi-objective optimization of daylighting performance and solar radiation for building geometry using a hybrid evolutionary algorithm.

Optimizing solar radiation and daylighting performance is a fundamental concern in architectural design, as these factors are directly linked to enhancing building energy efficiency and environmental comfort. This study seeks to balance solar radiation and daylighting performance through architectural geometry optimization, includes building's length, width, height, orientation, and mass distribution. Parametric modeling based on the additive and subtractive design generation algorithms included in EvoMass on the Grasshopper platform, with the goal of minimizing the solar radiation variation between summer and winter on building envelopes and maximizing useful daylight illuminance (UDI). A multi-objective evolutionary algorithm named Steady-State Island Evolutionary Algorithm (SSIEA) was applied to optimize the building geometry, ultimately yielding the Pareto front optimal solution. When compared to the reference building, the optimized design enables the building to achieve a more balanced solar radiation distribution across different seasons, resulting in a 26.89% improvement in performance. Additionally, the building's daylighting performance is enhanced by 19.85%. The results demonstrate that leveraging the Pareto front in the early stages of building geometric form design provides architects with effective strategies and solutions for geometry optimization, enabling performance-based design decisions in subsequent stages.