Genetic evolution vs. function approximation: Benchmarking algorithms for architectural design optimization
Graphical abstract Graphical Abstract AbstractThis article presents benchmark results from seven simulation-based problems from structural, building energy, and daylight optimization. Growing applications of parametric design and performance simulations in architecture, engineering, and construction...
Gespeichert in:
| Veröffentlicht in: | Journal of computational design and engineering Jg. 6; H. 3; S. 414 - 428 |
|---|---|
| 1. Verfasser: | |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Oxford University Press
01.07.2019
한국CDE학회 |
| Schlagworte: | |
| ISSN: | 2288-5048, 2288-4300, 2288-5048 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | Graphical abstract
Graphical Abstract
AbstractThis article presents benchmark results from seven simulation-based problems from structural, building energy, and daylight optimization. Growing applications of parametric design and performance simulations in architecture, engineering, and construction allow the harnessing of simulation-based, or black-box, optimization in the search for less resource- and/or energy consuming designs. In architectural design optimization (ADO) practice and research, the most commonly applied black-box algorithms are genetic algorithms or other metaheuristics, to the neglect of more current, global direct search or model-based, methods. Model-based methods construct a surrogate model (i.e., an approximation of a fitness landscape) that they refine during the optimization process. This benchmark compares metaheuristic, direct search, and model-based methods, and concludes that, for the given evaluation budget and problems, the model-based method (RBFOpt) is the most efficient and robust, while the tested genetic algorithms perform poorly. As such, this article challenges the popularity of genetic algorithms in ADO, as well as the practice of using them for one-to-one comparisons to justify algorithmic innovations.
Highlights
Benchmarks optimization algorithms on structural, energy, and daylighting problems.Benchmarks metaheuristic, direct search, and model-based optimization methods.Challenges the popularity of genetic algorithms in architectural design optimization.Presents model-based methods as a more efficient and reliable alternative. |
|---|---|
| ISSN: | 2288-5048 2288-4300 2288-5048 |
| DOI: | 10.1016/j.jcde.2018.09.001 |