Stochastic Grouping and Subspace-Based Initialization inDecomposition and Merging Cooperative Particle SwarmOptimization for Large-Scale Optimization Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Particle Swarm Optimization (PSO) algorithm is a meta-heuristic that has shown great proficiency in solving optimization problems. However, the PSO algorithm fails to scale efficiently for more complex, large-scale optimization problems (LSOPs). The Decomposition and Merging Cooperative-PSO variants (DCPSO & MCPSO) were introduced to improve the performance of PSO as the number of dimensions in the optimization problem increased. Though beneficial, the DCPSO and MCPSO algorithms do not address one of the main reasons for performance degradation in these large search spaces: particles leaving the search space. Research in this area has shown that it may be beneficial to focus on initially exploiting a small area of the search space, instead of exploring the entire search space. To achieve this, we implement the techniques of Subspace-Based Initialization (SBI), Stochastic Grouping (SG), and Increasing Stochastic Grouping (ISG) in concert with the existing DCPSO and MCPSO algorithms. Results show that the SBI algorithm variants outperform their vanilla counterparts across all dimensions tested (100, 500, 1000, 2000). The SG and ISG approaches are found to perform best at higher dimensions, outperforming the standard MCPSO and DCPSO algorithms at 1000 and 2000 dimensions.
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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 | 0.001 | 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