Optimizing Northern Goshawk Algorithm with Fuzzy Logic and Whale Algorithm Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scientists have initiated the examination of living behavioral patterns of organisms, with a primary focus on their quest for sustenance and evasion of predators to ensure their survival.This research endeavors to formulate mathematical models capable of emulating these behaviors, thereby empowering these models to address intricate and demanding mathematical quandaries.In this investigation, two distinct strategies were employed to enhance problem-solving capabilities.The first strategy entailed synergizing the North Goshawk Optimization Algorithm (NGOA) with fuzzy logic (FL).Fuzzy logic was leveraged to impart fuzziness to the initial population and allocate membership grades to all community elements within the confines of the fuzzy logic framework.The second strategy involved the integration of two hybridization approaches: the first through the community and the second via equations between the Fuzzy North Goshawk Optimization Algorithm (NGOA) and the Whale Optimization Algorithm (WOA).The proposed methodology was implemented across ten fundamental functions, revealing a marked superiority of the proposed algorithm when compared to the original version.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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