Evolving multi-objective optimization framework for early-stage building design: Improving energy efficiency, daylighting, view quality, and thermal comfort

Computational performance-driven design optimization (CPDDO) informs early building design decisions, enhancing projects' responsiveness to local climates. This paper reviews recent CPDDO studies, identifies prevalent gaps, and proposes a refined optimization framework. The framework stands out by: (1) integrating view quality alongside energy, daylight, and thermal comfort considerations, with a vector-simulation-based metric considering content, access and clarity; (2) incorporating users' adaptive behavior patterns in simulations; and (3) employing a hybrid weighting method to accommodate diverse project demands and support robust design decisions. This study applies the framework to optimize the shape and facade variables of a medium-sized office building in Guangzhou, Chongqing, Qingdao, Lanzhou, and Changchun, representing hot, warm, mixed, cool, and cold climates, respectively. Results highlight that geometry features (aspect ratio, orientation, window-to-wall ratio (WWR), and shading devices), as well as window and blinds constructions significantly impact energy, daylight, thermal comfort and view quality. Different climatic conditions, objective priorities, and facade orientations necessitate tailored design variables. Furthermore, certain findings challenge conventional recommendations; for instance, buildings in colder climates benefit from increased WWR, due to enhanced potential to harness solar radiation and improved view access, while high-performance envelope thermal settings mitigate heat transfer. These findings underscore the need for detailed, targeted research in early-stage design. The developed CPDDO framework proves effective and user-friendly, offering new possibilities for optimizing building performance, thus holds the potential to foster green, comfortable, and sustainable architecture in various practical applications.