A multiple learning moth flame optimization algorithm with probability-based chaotic strategy for the parameters estimation of photovoltaic models
Bibliographic record
Abstract
The parameters of the photovoltaic (PV) models affect the accuracy in the evaluation and control of PV systems. To estimate the parameters of various PV models accurately and reliably, we propose a multiple learning moth flame optimization algorithm with a probability-based chaotic strategy (MLMFO-PBCS). In MLMFO-PBCS, the multiple learning strategy effectively combines the information of flame and moth population in different stages of iteration, providing more chances for moths to update and supplying eminent exploration and exploitation capabilities. Moreover, a probability-based chaotic strategy is introduced to the global optimal solution on each iteration so that a promising solution can be established to update the worst moth, avoiding premature and enhancing the exploitation ability. The proposed MLMFO-PBCS has been used to evaluate parameters of different PV models including single diode, double diode, and PV module. Comprehensive experimental results indicate that MLMFO-PBCS is highly competitive on parameter estimations of PV models in accuracy, reliability, and convergence speed, compared with all compared algorithms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".