Research on Large-scale Multi-target Units Combination Model Based on IAFSA
Why this work is in the frame
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Bibliographic record
Abstract
Because the advantages of new energy units in the whole life cycle of economy and environmental protection have been gradually highlighted. In the large-scale multi-target unit combination problem, more consideration of the composition of new energy units has become the current hot spot. This paper establishes a large-scale multi-target unit combination model, considering the two goals of economic and environmental protection, as well as the power abandonment rate, rotating reserve, unit output and other constraints of new energy units. The multi-target problem is transformed into a single target problem by adopting a multi-target processing scheme based on linear weighting method. The Improved Artificial Fish Swarm Algorithm (IAFSA) iterative hybrid algorithm is proposed. Comparison with Artificial Fish Swarm Algorithm (AFSA) shows that the proposed algorithm can globally search better, achieves more convergence and faster in large-scale and multi-objective convergence conditions.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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