The research developed an urban design approach in which optimization systems are used to explore complex trade-offs related to environmental impact, social well-being, and economic viability.
Key research outcomes include the following:
The development of a design method, referred to as Evolutionary Urbanism, that leverages evolutionary algorithms, urban simulations, and cloud computing. Evolutionary algorithms were used to explore the trade-offs between multiple conflicting performance criteria. Advanced Parametric Information Modelling (PIM) techniques were used to generate design variants, and various existing simulation programs were used to evaluate performance criteria, including spatial analysis, energy consumption, daylighting, and structural integrity.
The enhancement of the Dexen system in order to facilitate parallel execution on the Amazon EC2 cloud. The whole systems was re-written to allow for easy deployment on cloud-based infrastructures. For end-users, a web-based UI was created for managing evolutionary jobs and tasks being executed remotely.
A set of case studies showing how the Evolutionary Urbanism design method can be used to evolve urban designs for a range of different high density housing typologies. The Figure above shows the evolution of housing developments using a point-block typology common in Asia.