Cooperative particle swarm optimization of passive microwave devices
Bibliographic record
Abstract
Abstract Particle swarm optimization (PSO) has lately become very popular in the electromagnetics domain. Although in some instances PSO shows a superior performance compared with other global optimization techniques, it is still computationally more expensive relative to classical gradient techniques. In this paper, a cooperative particle swarm optimization (CPSO) is adopted to achieve a faster convergence compared with the conventional PSO, while maintaining its main feature, which is the capability of finding global optimum. In order to deploy PSO more efficiently, the often neglected effect of the initial population on the overall convergence of PSO is discussed. It is shown that subdividing the space into grid cells and using random distribution within these cells will give the best results in terms of convergence speed. Different boundary conditions are tried on the CPSO algorithm. In order to verify the performance of the proposed algorithm, the algorithm is compared with the conventional PSO using six different objective functions. As a design example, an ultra‐wide‐band filter is designed. The results show a slightly faster convergence compared with the conventional PSO. The designed filter is fabricated and experimental results are also shown. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".