Approximated Unimodal Region Elimination Based Global Optimization Method for Engineering Design
Bibliographic record
Abstract
Computer analysis and simulation based design optimization requires more computationally efficient global optimization tools. In this work, a new global optimization algorithm based on design experiments, region elimination and response surface model, namely Approximated Unimodal Region Elimination Method (AUREM), is introduced. The approach divides the field of interest into several unimodal regions using design experiment data; identify and rank the regions that most likely contain the global minimum; form a response surface model with additional design experiment data over the most promising region; identify its minimum, remove this processed region, and move to the next most promising region. By avoiding redundant searches, the approach identifies the global optimum with reduced number of objective function evaluations and computation effort. The new algorithm was tested using a variety of benchmark global optimization problems and compared with several widely used global optimization algorithms. The experiments results present comparable search accuracy and superior computation efficiency, making the new algorithm an ideal tool for computer analysis and simulation black-box based global design optimization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".