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.
This paper presents computational methods for creating and improving a closed loop of design optimization and knowledge discovery in architecture. It first introduces a design knowledge-assisted optimization improvement method with the technique - offline simulation - to reduce the computing time and improve the efficiency of the design optimization process utilizing architectural domain knowledge. It then describes a new design knowledge discovery system where design knowledge can be discovered from optimization through an automatic data mining approach. The discovered knowledge has the potential to further help improve the efficiency of the optimization method, thus forming a closed loop of improving optimization and knowledge discovery. The demonstration and validation of both methods are presented in the context of a case study with parametric form-finding for a nursing unit design with two design objectives: minimizing the nurses' travel distance and maximizing daylighting performance in patient rooms.
The integration of building performance simulation and design optimization in the early stages of the architectural design process has attracted a high volume of research in recent years. However, both building simulation and design optimization require a significant amount of computing time, especially when there are multiple design objectives to achieve. In this paper, we present a technique-offline simulation-to effectively reduce the computing time in such design optimization problems. The validation of this method is presented in the context of a case study with parametric form-finding for a nursing unit design with two design objectives: minimizing the nurses' travel distance and maximizing daylighting performance in patient rooms. The results show that computing time can be reduced significantly during the simulation and optimization process. The technique presented is based on Genetic Algorithm (GA). The use of GA in architectural design has become a trend for design optimization. Currently, however, only the general method of GA is applied to architectural problems.This research provides a new type of study that utilizes architectural domain knowledge to customize GA techniques in order to significantly improve the design optimization process.
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