Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Advanced Engineering Informatics
An integrated approach to algorithmic design and environmental analysis
Proceedings of the 2011 Symposium on Simulation for Architecture and Urban Design
Multi-level interaction in parametric design
SG'05 Proceedings of the 5th international conference on Smart Graphics
Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design
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This research is built upon a previously established multidisciplinary design optimization (MDO) framework and further explores the impact of this framework on the early stages of design. Specifically, this paper addresses the potential of introducing a cloud-based approach to tackle geometrically complex design problems and to facilitate early stage design exploration. To address these interests two experiment sets are presented and then discussed in the context of the application of cloud-based computing. First, is a hypothetical scenario possessing complex geometry to understand how the existing established framework assists in the exploration of complex geometric design problems. Second, is a pedagogical benchmark case allowing for the observation of the human versus automated decision making process. By comparing these processes the impact of the established MDO approach on "designing-in performance" and the potential impact of applying cloud-based computing to the MDO framework can be revealed and discussed.