| id |
ecaade2018_323 |
| authors |
Newton, David |
| year |
2018 |
| title |
Multi-Objective Qualitative Optimization (MOQO) in Architectural Design |
| doi |
https://doi.org/10.52842/conf.ecaade.2018.1.187
|
| source |
Kepczynska-Walczak, A, Bialkowski, S (eds.), Computing for a better tomorrow - Proceedings of the 36th eCAADe Conference - Volume 1, Lodz University of Technology, Lodz, Poland, 19-21 September 2018, pp. 187-196 |
| summary |
Architectural design problems are often multi-objective in nature, involving both qualitative and quantitative objectives. Previous research has focused exclusively on the development of multi-objective optimization algorithms that work with multiple quantitative objectives. No previous research has looked at the topic of multi-objective qualitative optimization (MOQO), in which multiple qualitative objectives are optimized simultaneously. This research addresses MOQO through the development of a unique multi-objective optimization algorithm for the conceptual design phase that uses three-dimensional convolutional neural networks (3D CNNs) to measure user-defined qualities in architectural massing models. |
| keywords |
multi-objective optimization; generative design; multi-objective qualitative optimization; algorithmic design |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (10,625,919 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:58 |
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