SLL Reduction in Linear Antenna Arrays by Genetic Algorithm, Flower Pollination Algorithm, and Grey Wolf Optimization with Iteration and Population Parameters
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".