Modified bacterial foraging algorithm for optimum economic dispatch
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a modified bacterial foraging algorithm (MBFA) to solve the economic dispatch problem (ED) considering valve-point effects and power losses. The basic bacterial foraging algorithm (BFA) is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The original BFA has been successfully used for small scale optimization problems. On the other hand, when it is applied to larger constrained problems, it shows poor convergence characteristics. To overcome these difficulties due to the complexity and high-dimensionality of the search space of the ED problem, important modifications are introduced to enhance the performance of the BFA. A dynamic chemotactic step is proposed in order to improve the global and local search ability of the algorithm. The MBFA is tested using five case studies. Results are compared against those obtained by other algorithms previously reported in the literature.
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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 | 0.000 | 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