The Effect of Probability Distributions on the Performance of Quantum Particle Swarm Optimization for Solving Dynamic Optimization Problems
Bibliographic record
Abstract
The quantum particle swarm optimization (QPSO) algorithm was developed to address the limitations of the traditional particle swarm optimization (PSO) algorithm in dynamic environments. Some particles in the QPSO algorithm are chosen as "quantum" particles, and the positions of these are sampled uniformly within a radius (i.e., A hyper sphere) centred around the global best particle. The remainder of particles follow standard PSO behaviour. This paper proposes sampling various alternative probability distributions to update the positions of quantum particles. Ten probability distributions are examined on dynamic environments with varying dimensionalities, temporal change severities, and spatial change severities, with both single-peak and five-peak environments considered. Results indicated that the most effective distribution to use is dependent upon the type of dynamism present. In general, it was observed that a small quantum radius was preferable to a large radius, indicating that exploitation is more beneficial than exploration with regards to QPSO performance. Finally, despite having been commonly used in various QPSO applications, the performance of the uniform distribution was found to be sub-par.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".