Reference Point-Based Particle Sub-Swarm Optimization
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a novel optimization method named reference point-based particle sub-swarm optimization (RPB-PSWO) is presented. RPB-PSWO utilizes the particle position update method of PSO and with the non-dominance and diversity selection methods of NSGA-II. The multi-objective optimizer utilizes a reference point-based system to allocate particles into an equidistant sub-swarm, in which particles are attracted to a pareto optimal solution in that sub-swarm. To encourage diversity and avoid local minima, density and turbulence factors are included. RPB-PSWO is capable of optimizing problems with many dependent variables, as the position update method of PSO inherently preserves dependent relationships, but suffers from an increased computation cost compared to NSGA-II. The proposed algorithm, although less computationally efficient, is capable of creating diverse pareto front solutions for standardized and custom 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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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