Improving firefly algorithm performance using fuzzy logic
Bibliographic record
Abstract
Exploration and exploitation are two strategies used to search the problem space in Evolutionary Algorithms (EAs). To significantly increase the performance of these optimization techniques in terms of the solution optimality is to strike the right balance between exploration and exploitation. Firefly is one of the most favored EAs. In this study, we introduce an entire fuzzy system to tune dynamically the firefly parameters in order to keep the exploration and exploitation in balance in each of the searching steps. A serious concern of EAs is to be stuck in local optimum solutions. The proposed fuzzy controller helps the firefly algorithm to converge to the optimal solution and escape from local optimums. To evaluate the efficiency of the fuzzy-based firefly algorithm, we conduct experiments on a set of high dimensional benchmark functions. The goal here is to compare the new firefly method with the standard firefly and well-known nature-inspired optimization algorithms. The results of the experiments show the superiority of the proposed Fuzzy firefly algorithm over the standard one.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".