Optimizing creatively in multi-objective optimization
Bibliographic record
Abstract
Designers will always face the challenge of designing well-performing buildings using what are often conflicting and competing objectives. Early stage design decisions influence significantly the final performance of a building and designers are often unable to explore large numbers of design alternatives with respect to the performative criteria set for the project. This research outlines a optimization workflow' using a Multi-Objective Optimization (MOO) engine called Octopus that runs within Grasshopper3D, a parametric modeling tool, and simulation software DIVA for daylight factor analysis. The workflow utilizes a optimization tool' which allows the designer to explore, sort and filter solutions, and analyze both quantitatively and qualitatively the trade-offs of the resultant design solution space. It enables the designer to visually compare alternative solutions in a gallery and subsequently analyze trade-offs through a radar-based chart, parallel coordinate plot graphs and conditional domain searches. This feedback tools allows the designer to quickly and efficiently identify potential solutions for either design development or to select preferred solutions for further optimization, i.e. optimizing creatively'. A retrospective design case study, the De Rotterdam' building, is used to demonstrate the application of the tools. The workflow demonstrates the ability to reduce design latency and to allow for better understanding of design solutions. Additional research is needed to better understand the application of MOO in the early stages of design; and the further improvement of the creative optimization tools to accommodate the designer's need for a more dynamic and synergistic process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".