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Dexen: A scalable and extensible platform for experimenting with population-based design exploration algorithms

Published online by Cambridge University Press:  07 October 2015

Patrick Janssen
Patrick Janssen*
Affiliation:
Department of Architecture, National University of Singapore, Singapore
*
Reprint requests to: Patrick Janssen, Department of Architecture, National University of Singapore, 4 Architecture Drive, Singapore, 117 566. E-mail: [email protected]
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Abstract

A platform for experimenting with population-based design exploration algorithms is presented, called Dexen. The platform has been developed in order to address the needs of two distinct groups of users loosely labeled as researchers and designers. Whereas the researchers group focuses on creating and testing customized toolkits, the designers group focuses on applying these toolkits in the design process. A platform is required that is scalable and extensible: scalable to allow computationally demanding population-based exploration algorithms to be executed on distributed hardware within reasonable time frames, and extensible to allow researchers to easily implement their own customized toolkits consisting of specialized algorithms and user interfaces. In order to address these requirements, a three-tier client–server system architecture has been used that separates data storage, domain logic, and presentation. This separation allows customized toolkits to be created for Dexen without requiring any changes to the data or logic tiers. In the logic tier, Dexen uses a programming model in which tasks only communicate through data objects stored in a key-value database. The paper ends with a case study experiment that uses a multicriteria evolutionary algorithm toolkit to explore alternative configurations for the massing and façade design of a large residential development. The parametric models for developing and evaluating design variants are described in detail. A population of design variants are evolved, a number of which are selected for further analysis. The case study demonstrates how evolutionary exploration methods can be applied to a complex design scenario without requiring any scripting.

Keywords

Design ExplorationDesign OptimizationDistributed ComputingEnd-User ProgrammingEvolutionary Algorithms
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 443 - 455
Copyright
Copyright © Cambridge University Press 2015 

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