Operative generative design using non-dominated sorting genetic algorithm II (NSGA-II)
Elnaz Tafrihi Bailey, Luisa Caldas
Abstract
Massing studies during the early stages of architectural design play an essential role in determining the final building’s performance across design objectives. This paper aims to answer the question: How can early-stage architectural design workflows be translated into a generative design process to create valuable massing solutions? In response, a new application of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) using the Pymoo framework is proposed for the field of Operative Design. Nine experiments are discussed that test the algorithm’s geometry optimization capabilities based on objective functions reflecting common architectural design goals, including Floor Area Ratio (FAR), Non-Passive Zone (NPZ), Roofs and Best Oriented Surfaces (RBOS), and Usable Open Space (UOS). Selected cases are visualized among non-dominated solutions in each experiment demonstrating the trade-offs between different objectives while programmatically generating successful building designs. In the future, the proposed generative design workflow can be implemented to run optimizations independently from other software within immersive environments.