An Effective Application of Bacteria Quorum Sensing and Circular Elimination in MOPSO
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, an approach that incorporates a turbulence mechanism and a circular elimination strategy is presented to strengthen the performance of multi-objective particle swarm optimization (MOPSO). For convergence enhancement, the turbulence mechanism derived from bacteria quorum sensing behavior is introduced to MOPSO to preserve the swarm diversity. Meanwhile, the circular elimination strategy is used to select particles for next iteration for better distribution of the Pareto-optimal solutions. The improved MOPSO algorithm has been tested on a set of benchmark functions and compared with representative multi-objective optimization algorithms. Simulation results illustrate that the algorithm outperforms the other algorithms on convergence while keep good spread performance, and could be used as an effective global optimization tool.
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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 | 0.000 |
| 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