| id |
caadria2018_228 |
| authors |
Newton, David |
| year |
2018 |
| title |
Accommodating Change and Open-Ended Search in Design Optimization |
| doi |
https://doi.org/10.52842/conf.caadria.2018.2.175
|
| source |
T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping - Proceedings of the 23rd CAADRIA Conference - Volume 2, Tsinghua University, Beijing, China, 17-19 May 2018, pp. 175-184 |
| summary |
Many real-world architectural multi-objective problems (MOPs) are dynamic and may have objectives, decision variables, and constraints that change during the optimization process. These problems are known as dynamic MOPs (DMOPs). Dynamic multi-objective evolutionary algorithms (DMOEAs) have emerged in the fields of optimization, operations research, and computer science as one way to address the challenges posed by DMOPs. DMOEAs offer new capacities for exploration and interaction with the designer, but they have not yet been studied in the field of architecture. This research addresses these issues through the development of a unique interactive DMOEA-based design tool for the conceptual design phase. We propose a new modification to the popular nondominated sorting genetic algorithm II (NSGA-II), that we call the dynamic progressive for architecture NSGA-II (DPA-NSGA-II). We show that DPA-NSGA-II outperforms NSGA-II in finding novel solutions. |
| keywords |
algorithmic design; multi-objective optimization; evolutionary computation; parametric design; generative design |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (10,068,063 bytes) |
| references |
Content-type: text/plain
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Archer, Bruce L (1968)
 The Structure of Design Processes
, Ph.D. Thesis, Royal College of Art
|
|
|
|
|
Azzouz, R, Bechikh, S and Said, LB (2017)
Dynamic multi-objective optimization using evolutionary algorithms: a survey
, Datta, R and Gupta, A (eds), Recent Advances in Evolutionary Multi-objective Optimization, Springer, pp. 31-70
|
|
|
|
|
Brintrup, AM, Ramsden, J, Takagi, H and Tiwari, A (2008)
Ergonomic chair design by fusing qualitative and quantitative criteria using interactive genetic algorithms
, IEEE Transactions on Evolutionary Computation, 12(3), pp. 343-354
|
|
|
|
|
Chong, YT, Chen, CH and Leong, KF (2009)
A heuristic-based approach to conceptual design
, Research in Engineering Design, 20(2), pp. 97-116
|
|
|
|
|
Coello, CAC, Van Veldhuizen, DA and Lamont, GB (2002)
Evolutionary algorithms for solving multi-objective problems
, Springer
|
|
|
|
|
Deb, K, Pratap, A, Agarwal, S and Meyarivan, T (2002)
A fast and elitist multiobjective genetic algorithm: NSGA-II
, IEEE transactions on evolutionary computation, 6(2), pp. 182-197
|
|
|
|
|
Duffy, A, Andreasen, M, MacCallum, K and Reijers, L (1993)
Design coordination for concurrent engineering
, Journal of Engineering Design, 4(4), pp. 251-265
|
|
|
|
|
Gregory, SA (2013)
The design method
, Springer
|
|
|
|
|
Mueller, CT and Ochsendorf, JA (2015)
Combining structural performance and designer preferences in evolutionary design space exploration
, Automation in Construction, 52, pp. 70-82
|
|
|
|
|
Muyl, F, Dumas, L and Herbert, V (2004)
Hybrid method for aerodynamic shape optimization in automotive industry
, Computers & Fluids, 33(5), pp. 849-858
|
|
|
|
|
Obayashi, S, Sasaki, D, Takeguchi, Y and Hirose, N (2000)
Multiobjective evolutionary computation for supersonic wing-shape optimization
, IEEE transactions on evolutionary computation, 4(2), pp. 182-187
|
|
|
|
|
Turrin, M, von Buelow, P, Kilian, A and Stouffs, R (2012)
Performative skins for passive climatic comfort
, Automation in Construction, 22, pp. 36-50
|
|
|
|
|
Von Buelow, P (2012)
ParaGen: Performative Exploration of generative systems
, Journal of the International Association for Shell and Spatial Structures, 53(4), pp. 271-284
|
|
|
|
|
Wang, J (2001)
Ranking engineering design concepts using a fuzzy outranking preference model
, Fuzzy sets and systems, 119(1), pp. 161-170
|
|
|
|
| last changed |
2022/06/07 07:58 |
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