Abstract
The imperative for sustainable built environments necessitates advanced methodologies to optimize building performance. This research explores the integration of algorithmic optimization techniques into the early stages of architectural design to improve energy efficiency, occupant comfort, and overall environmental impact. Traditional design processes often struggle to comprehensively evaluate the complex interplay of variables affecting building performance. This study proposes a framework that leverages parametric design and computational optimization algorithms to systematically explore the design space and identify high-performing solutions. We review existing approaches to algorithmic optimization in building design, highlighting their strengths and limitations, particularly concerning multi-objective considerations and the integration of passive design strategies (Omrany & Marsono, 2016; Konis et al., 2016). The methodology employs a simulation-driven optimization process, integrating building performance simulation tools with optimization algorithms such as genetic algorithms and particle swarm optimization (Deb & Padhye, 2013; Machairas et al., 2014). A case study involving a typical residential building typology is presented, where key design parameters like envelope characteristics, window-to-wall ratio, and shading devices are optimized for energy consumption and daylighting. Results indicate significant potential for performance improvements through this approach, with optimized designs demonstrating a notable reduction in energy demand and enhanced indoor environmental quality compared to baseline models (Ji et al., 2023; Sonta et al., 2021). The findings underscore the efficacy of algorithmic optimization as a powerful tool for achieving sustainable building objectives, offering designers a data-driven approach to decision-making early in the design process. Future work should focus on expanding the scope of optimization to include a broader range of performance metrics and material considerations (Han et al., 2022).