Particle swarm optimisation for the design of two-connected networks with bounded rings
Why this work is in the frame
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Bibliographic record
Abstract
The particle swarm optimisation (PSO) is a stochastic population-based global optimisation technique modelled on the social behaviour of bird flocks or fish schooling. This paper investigates the use of PSO for designing minimum cost two-connected networks such that the shortest cycle to which each edge belongs to does not exceed a given length. PSO is a relatively new metaheuristic in which particles were originally designed to handle a continuous solution space. Given that the topological network design problem is a highly constrained discrete combinatorial optimisation, we modify the particle position representation and the particle velocity update rule by introducing an oscillating mechanism to better adapt a standard PSO for the problem. We provide numerical results based on randomly generated graphs found in the literature and compare the solution quality with that of tabu search and genetic algorithms. An empirical study for network sizes up to 30 nodes and a comparison with tabu search and genetic algorithms shows the potential of using PSO for the problem. To the best of our knowledge, this is the first attempt to implement particle swarm optimisation for the aforementioned problem.
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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 | 0.000 |
| Open science | 0.001 | 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