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A fast genetic algorithm for solving architectural design optimization problems

Published online by Cambridge University Press:  07 October 2015

Zhouzhou Su  and
Wei Yan
Zhouzhou Su
Affiliation:
Department of Architecture, Texas A&M University, Texas A&M University, College Station, Texas, USA
Wei Yan*
Affiliation:
Department of Architecture, Texas A&M University, Texas A&M University, College Station, Texas, USA
*
Reprint requests to: Wei Yan, Department of Architecture, Langford A 406, Texas A&M University, College Station, TX 77843, USA. E-mail: [email protected]
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Abstract

Building performance simulation and genetic algorithms are powerful techniques for helping designers make better design decisions in architectural design optimization. However, they are very time consuming and require a significant amount of computing power. More time is needed when two techniques work together. This has become the primary impediment in applying design optimization to real-world projects. This study focuses on reducing the computing time in genetic algorithms when building simulation techniques are involved. In this study, we combine two techniques (offline simulation and divide and conquer) to effectively improve the run time in these architectural design optimization problems, utilizing architecture-specific domain knowledge. The improved methods are evaluated with a case study of a nursing unit design to minimize the nurses’ travel distance and maximize daylighting performance in patient rooms. Results show the computing time can be saved significantly during the simulation and optimization process.

Keywords

Architectural Design OptimizationBuilding Performance SimulationComputing TimeGenetic Algorithm
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 457 - 469
Copyright
Copyright © Cambridge University Press 2015 

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