Footprinting the Behaviour of Particle Swarm Optimization with Increasing Dimensionality
Bibliographic record
Abstract
It is well documented that the performance of Particle Swarm optimization changes (deteriorates) with increasing dimensionality of the search space. It is less well documented that the operational behaviour of Particle Swarm optimization (PSO) can also change with increasing dimensionality. The current study documents these changes by using Self-Organizing Maps to “footprint” the operation of PSO. Increasing dimensionality produces key changes to the footprints in a multi-modal search space, but these changes do not occur in a unimodal search space. A deeper analysis is then conducted to connect the observed changes in footprints in multi-modal search spaces to changes in the operational behaviour of PSO caused by the effects of increasing dimensionality. The collected data indicate a correlation between the performance degradation of PSO and the decreased rates of success of exploratory moves, and this trend can be isolated from the effects of the exponentially increasing search space volumes that are produced in higher dimensions for continuous domain search spaces.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".