SLL Reduction in Linear Antenna Arrays by Genetic Algorithm, Flower Pollination Algorithm, and Grey Wolf Optimization with Iteration and Population Parameters
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Bibliographic record
Abstract
The combination of antenna arrays with optimization algorithms aims to minimize SLL, Linear antenna arrays are an extensively used electromagnetic system in modern wireless communication.The improvement algorithms are the genetic algorithm GA, the flower pollination algorithm FPA, and the grey wolf optimization GWO.This has been implemented to reduce SLL and communicate the signal to the right place and the highest efficiency with the greatest amount of energy and by reaching the best solution.antenna arrays engineering was arranged in linearity and implemented in different numbers of elements, i.e.8,16,32,64,128, and 256 elements, Each algorithm has criteria that affect the reduction of SLL, In GA when considering the influential parameters represented by iteration, population size, and max stall iteration, the best effect is iteration where SLL is reduced to -32.9523dB and at 16-element at iteration 50.FPA has many influential parameters representing iteration, population size, probability, and flower attraction rate.The best of these effects is iteration.SLL reduced to -35.0696dB at iteration 300 and at 64element.In GWO the influential parameters are iteration and population size the best effect, it was concluded, is iteration as well, which has reduced SLL to -32.8479dB at 8-element in iteration 140.
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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 | 0.001 |
| 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