Interactive Visualization of Dynamic and High-Dimensional Particle Swarm Behavior
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
Particle swarm optimization (PSO) is a robust and popular stochastic population-based global optimization method that simulates social behavior among independent agents (particles). PSO is increasingly used to solve difficult high-dimensional and dynamic problems, where the global optima change over time. To better address the challenges inherent in these problems, interactive visualization is employed to study the behavior of these agents. In this paper, PSO variants are used to optimize high-dimensional and dynamic non-convex cost functions. Dimension reduction allows the application of state-of-the-art interactive scientific visualization techniques to study the behaviors and dynamic trends of the swarms, and to uncover patterns and algorithm mechanics. Problems in the search and weaknesses in the algorithms can be more easily identified, thereby facilitating enhancements for domain-specific problems. Results suggest that interactive visualization aids understanding of high-dimensional socially-based modeling.
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 | 0.001 |
| 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