Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem
Why this work is in the frame
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Bibliographic record
Abstract
A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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