Cooperative Asynchronous Parallel Particle Swarm Optimization for Large Dimensional 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
Metaheuristics have been very successful to solve NP-hard optimization problems. However, some problems such as big optimization problems are too expensive to be solved using classical computing. Naturally, the increasing availability of high performance computing (HPC) is an appropriate alternative to solve such complex problems. In addition, the use of HPC can lead to more accurate metaheuristics if their internal mechanisms are enhanced. Particle swarm optimization (PSO) is one of the most know metaheuristics and yet does not have many parallel versions of PSO which take advantage of HPC via algorithmic modifications. Therefore, in this article, the authors propose a cooperative asynchronous parallel PSO algorithm (CAPPSO) with a new velocity calculation that utilizes a cooperative model of sub-swarms. The asynchronous communication among the sub-swarms makes CAPPSO faster than a parallel and more accurate than the master-slave PSO (MS-PSO) when the tested big problems.
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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 | 0.002 | 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