A study of optimal topologies in swarm intelligence
Why this work is in the frame
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Bibliographic record
Abstract
The Particle Swarm Optimization (PSO) algorithm was proposed by Kennedy and Eberhart to solve unconstrained, nonlinear optimization problems. This paper examines the merits of different neighbourhood topologies using the original PSO algorithm. The global, ring, star, torus, trees, and a newly proposed hierarchical topologies are tested against the Sphere, Rosenbrock, Rastrigin, and Griewank functions. The study looks at the number of iterations until the function converges (when the fitness function does not change by more than a convergence error for 50 iterations) and the mean fitness achieved by each test. The results indicate that the torus and a Gov-7 topologies performs well for all functions tested due to the degrees of separation and multiple paths for information flow that allow information about a good solution to be propagated to the rest of the particles. This work also shows how special nodes can serve as filters that reject local solutions in swarm topologies. This work furthers the understanding of swarms and the information flow through the network.
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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 | 0.001 | 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