A New Optimization Algorithm Based on the Behavior of BrunsVigia Flower
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Bibliographic record
Abstract
The most challenging problem of these days is dealing with the high dimensionality and any progress in this area is very welcomed. This paper is an attempt to propose a new optimization algorithm that performs well in problems with high dimensions. As the result, a new optimization algorithm based on the behavior of a special flower named as Brunsvigia is proposed. The distinguishing property of this algorithm is its simplicity as well as efficiency in angular movement. The special seeding behavior of this flower has been simulated to propose a search algorithm named as Brunsvigia Optimization Algorithm (BVOA) which has two major phases as information sharing through pollination and solution movement based on the head tumbling behavior of the flower in nature. The performance of the proposed algorithm is evaluated on some test functions used in CEC2005 and the results are compared with four other optimization algorithms. The results show that the proposed algorithm has a better performance in high dimensions.
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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 | 0.001 | 0.000 |
| 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) | 0.004 | 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