Multi-Objective Optimization of the Layout of Tall Building Sites in Dense Urban Configurations for Improved Sustainability Outcomes

Multi-objective evolutionary algorithms have long been used by architects to find objective solutions to complex building problems involving trade-offs implicit in sustainable building design. At a larger scale, urban designers have created a variety of tools to improve sustainability in urban-and-larger scale design. However, to date, fewer studies have focused on improving sustainability outcomes at the “in between” scale of the neighborhood and urban site. Existing scholarship on optimization at this scale has tended to take a narrow view of sustainability. We seek to expand the implementation of multi-objective evolutionary algorithms to this sometimes overlooked scale while taking a broad view of sustainability which includes social, environmental, and economic design factors. In doing so, we argue this optimization method is uniquely well suited to help designers balance the sometimes competing demands of multiple axes of sustainability which are applicable to design at this larger-than-building scale. In demonstrating the application of such an algorithm to a hypothetical problem in Chicago, we find the method offers a promising way of narrowing potential design solutions. Finally, we discuss the suitability of the solutions generated, the virtues and shortcomings of the method, and offer areas for future study.