A Nature-inspired Fully Enhanced Hybrid Algorithm Based on Intra-group Competition Mechanism
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
The flower pollination algorithm exhibits notable strengths, including robust search capabilities, minimal parameter requirements, and a straightforward architecture, but due to the randomness of its local search, it leads to slow convergence. Comparatively, the raccoon optimization algorithm does not require parameter adjustment, and the local search range is gradually reduced over time, ensuring the algorithm's effectiveness and convergence. However, for solving high-dimensional complex problems, the global search time is too long to reach the optimal global solution. Therefore, This study introduces a novel coati flower pollination algorithm incorporating an intra-group competition mechanism, effectively integrating the global exploration capabilities of FPA with the local exploitation characteristics of COA. The algorithm divides the population by k-means clustering to improve diversity and utilizes the competition mechanism to promote information exchange among individuals. For winning and losing individuals, the improved flower pollination algorithm and coati optimization algorithm are used for iterative updating, respectively, and adaptive polynomial mutation is introduced to avoid local optima. The superiority of the algorithm is verified on the CEC2017.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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