Expansion of Particle Multi-Swarm Optimization
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
For improving the search ability and performance of elementary multiple particle swarm optimizers, we, in this paper, propose a series of multiple particle swarm optimizers with information sharing by introducing a special strategy,called multi-swarm information sharing. The key idea, here, is to add a special confidence term into the updating rule of the particle's velocity by the best solution found out by the particle multi-swarm search. This is a new type approach for the technical development and evolution of particle multi-swarm optimization itself. In order to confirm the effectiveness of the information sharing strategy in the proposed particle multi-swarm search, several computer experiments of dealing with a suite of benchmark problems are carried out. For investigating the performance and efficiency of these proposed methods, we compare their search ability and performance, respectively. The obtained experimental results show that the proposed methods have better search ability and performance than those methods without the strategy. And we still decide the best value of adding the new confidence coefficient to the multi-swarm for dealing with the given optimization problems.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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