Informed, In Form

This study aims to address the architectural challenges presented by Multi-Objective Design Optimisation by developing a tool that assists designers in balancing multiple, often conflicting, design factors. The first (foundational) part of the study involves employing genetic algorithms to optimise key variables such as building cost, sustainability, view quality, and sun exposure. Using a multi-objective optimisation algorithm (MOOA), specifically the NSGA-II genetic algorithm, this process is facilitated by the Wallacei plugin for Rhino’s Grasshopper. The second (localised) part of the study then shows how specific local factors can be accommodated, and focuses on the specific environmental conditions of Wellington, New Zealand to illustrate the approach. Consequently, additional simulations are used to refine the designs to accommodate local challenges, such as wind environments. By combining these foundational and localised methods, the study provides a tool that not only optimises building forms but also adapts them to specific site conditions, enabling designers to make more informed, context-sensitive decisions that contribute to sustainable urban development.

Customised computational design that requires coding or plug-in assembly is often overlooked in professional practice, largely due to the learning curve. In a competitive environment, many designers hesitate to invest time in mastering new technologies, instead relying on purpose-made computational design environments such as Revit. However, as a new generation of computationally competent designers emerges, they often encounter the challenges of improving designs from the past, where the absence of advanced computational tools limited the ability to optimise for quality-of-life considerations. This responsibility necessitates that designer’s arm themselves with the most advanced tools and methodologies at their disposal, ensuring they are fully prepared to meet the complex challenges of modern design.