A fast genetic algorithm for solving architectural design optimization problems

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 w...

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Bibliographic Details
Published in:AI EDAM 2015-11, Vol.29 (4), p.457-469
Main Authors: Su, Zhouzhou, Yan, Wei
Format: Article
Language:English
Subjects:
Algorithms
Architecture
Buildings
Computational efficiency
Computer simulation
Computing time
Design analysis
Design engineering
Design optimization
Genetic algorithms
Nurses
Simulation
Special Issue Articles
Studies
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Summary: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.
ISSN:0890-0604
1469-1760
DOI:10.1017/S089006041500044X