Improving Zebra Optimization Algorithm via Fitness‐Distance Balance Strategy: Application to <scp>AVR</scp> ‐ <scp>LFC</scp> System
Bibliographic record
Abstract
ABSTRACT This study proposes the FDB‐ZOA algorithm, which is an improved version of the Zebra Optimization Algorithm (ZOA) with the Fitness‐Distance Balance (FDB) strategy to enhance the exploration and exploitation balance. The developed algorithm was tested on CEC2020 benchmark functions and compared with 13 different state‐of‐the‐art meta‐heuristic algorithms, including ZOA. The comparisons were supported by mean success, standard deviation, box plots, convergence curves, and Wilcoxon and Friedman tests; FDB‐ZOA demonstrated superior performance in all dimensions. Additionally, the algorithm's application potential has been demonstrated through parameter optimization of FOPID and FOPI‐FOPD controllers in AVR‐LFC systems, with results validated via time domain analysis, robustness tests, and OPAL‐RT‐based real‐time simulations. The findings obtained indicate that FDB‐ZOA is a strong candidate solution from both theoretical and practical perspectives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".