Comparative Study of PSO, GA, and ACO for Optimizing Dielectric Performance in Fly Ash Filled Silicone Rubber
Bibliographic record
Abstract
This study investigates the optimization of coal fly ash composition as a filler in Silicone Rubber (SiR) insulator materials, aiming to enhance their dielectric characteristics. Compositional optimization was achieved by evaluating and comparing three advanced meta-heuristic algorithms Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), using the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) as performance metrics. The utilized fly ash, containing dominant silica, alumina, and iron oxides, was directly incorporated into the SiR matrix. Results indicate that, compared to PSO, GA and ACO exhibited superior performance and consistency. Specifically, for Relative Permittivity, the optimal composition of 80% yielded the lowest errors with GA and ACO (RMSE = 0.0751; MAPE = 0.9044). For Hydrophobicity, these two algorithms showed superior accuracy in the RMSE metric (RMSE = 0.8883) at 15.39% loading. These findings underscore the scientific contribution of this study by establishing the superior reliability of GA and ACO for optimizing fly ash composition in SiR, thus providing a robust analytical methodology to advance the use of industrial waste for high-performance dielectric materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".