Synthesis of Nonuniform Array Antennas Using Particle Swarm Optimization
Bibliographic record
Abstract
As a newly discovered evolutionary algorithm, the particle swarm optimization algorithm has been widely used in the synthesis of array antennas, while it is seldom used in the synthesis of nonuniform array antennas. Two different nonuniform array antennas are optimized by binary particle swarm optimization and real particle swarm optimization in this article, which depicts the application of particle swarm optimization in the synthesis of nonuniform array antennas. Lower peak side-lobe level with uniform excitation can be obtained using this method. Meanwhile, the method of minimizing variable-searching space that can improve the efficiency of algorithm is used in particle swarm optimization. Compared with the standard genetic algorithm and the modified real genetic algorithm, particle swarm optimization shows high performance in the synthesis of nonuniform array antennas. To demonstrate the universality of the algorithm, a nonuniform circular array and a sparse linear array with a directional element are synthesized as well.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".