Cooperative particle swarm optimization of passive microwave devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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) | 0.000 | 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