Particle swarm optimisation with simple and efficient neighbourhood search strategies
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Bibliographic record
Abstract
This paper presents a novel particle swarm optimiser (PSO) called PSO with simple and efficient neighbourhood search strategies (NSPSO), which employs one local and two global neighbourhood search strategies. By this way, one strong and two weak locality perturbation operators are embedded in the standard PSO. The NSPSO consists of two main steps. First, for each particle, three trail particles are generated by the mentioned three neighbourhood search strategies, respectively. Then, the best one among the three trail particles is selected to compete with the current particle, and the fitter one is accepted as a current particle. In order to verify the performance of NSPSO, it experimentally has been tested on 12 unimodal and multimodal benchmark functions. The results show that NPSO significantly outperforms other seven PSO variants.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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