An integrative TLBO-driven hybrid grey wolf optimizer for the efficient resolution of multi-dimensional, nonlinear engineering problems
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
This research article introduces a hybrid optimization algorithm, referred to as Grey Wolf Optimizer-Teaching Learning Based Optimization (GWO-TLBO), which extends the Grey Wolf Optimizer (GWO) by integrating it with Teaching-Learning-Based Optimization (TLBO). The benefit of GWO is that it explores potential solutions in a way similar to how grey wolves hunt, but the challenge with this approach comes during fine-tuning, where the algorithm settles too early on suboptimal results. This weakness can be compensated by integrating TLBO method into the algorithm to improve its search power of solutions as in teaches students how to learn and teachers are knowledge felicitator. GWO-TLBO algorithm was applied for several benchmark optimization problems to evaluate its effectiveness in simple to complex scenarios. It is also faster, more accurate and reliable when compare to other existing optimization algorithms. This novel approach achieves a balance between exploration and exploitation, demonstrating adaptability in identifying new solutions but also quickly zoom in on (near) global optima: this renders it a reliable choice for challenging optimization problems according to the analysis and results.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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