Quantum Multi-guide Particle Swarm Optimisation for Dynamic Multi-objective Optimisation Problems
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The multi-guide particle swarm optimisation (MGPSO) algorithm, originally developed for static multi-objective optimisation problems (SMOPs), has been recently adapted for dynamic multi-objective optimisation problems (DMOPs). The MGPSO is a multi-swarm approach where each subswarm optimises one of the objectives. It uses a bounded, crowding distance archive implementation that is managed at each environment change. This paper further adapts the MGPSO for DMOPs by proposing alternative quantum particle swarm optimisation (QPSO) strategies to allow efficient tracking of the changing Pareto-optimal set (POS) and Pareto-optimal front (POF). Specifically, the self-adaptive QPSO and the parent-centric crossover (PCX) QPSO are explored with varying quantum proportions of particles. A total of twenty-nine benchmark functions and six performance measures were implemented to evaluate the performance of the QPSO approaches. The experiments were run against five environment types with varying temporal and spatial severities. The best QPSO strategy was then compared with other state-of-the-art dynamic multi-objective optimisation algorithms (DMOAs). An extensive empirical analysis shows that MGPSO with 10% proportion of self-adaptive quantum particles achieves very competitive and oftentimes better results when compared with other DMOAs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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