Statistical convergence analysis of ACO — NM for PID controller tuning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optimal controller and anti-windup tuning can be identified to a hard optimization problem and be solved by metaheuristics. Since metaheuristics' performance is based on the balance between the diversification and intensification processes obtained by adjusting the method parameters, it is important to set it adequately to provide a high quality solution. A statistical Ant Colony Optimization (ACO) analysis is proposed to establish the quality of the solution reached with regard to the number of ants and the number of objective function evaluations. Sensitivity curves to the number of ants and number of function evaluations for two different discretization search space are presented. For a lower number of function evaluations for ACO, a better starting point for the Nelder-Mead (NM) local search has been determined. The final system response is comparable to the previous ACO-NM algorithm for almost two times less evaluations of the objective function.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it