Dynamic adaptive bacterial foraging algorithm for optimum economic dispatch with valve-point effects and wind power
Bibliographic record
Abstract
This study presents a dynamically adapted bacterial foraging algorithm (BFA) to solve the economic dispatch (ED) problem considering valve-point effects and power losses. In addition, wind power is included in the problem formulation. Renewable sources and wind energy in particular have recently been getting more interest because of various environmental and economical considerations. The original BFA is a recently developed evolutionary optimisation technique inspired by the foraging behaviour of the Escherichia coli bacteria. The basic BFA has been successfully implemented to solve small optimisation problems; however, it shows poor convergence characteristics for larger constrained problems. To deal with the complexity and high-dimensioned search space of the ED problem, essential modifications are introduced to enhance the performance of the algorithm. The basic chemotactic step is adjusted to have a dynamic non-linear behaviour in order to improve balancing the global and local search. The stopping criterion of the original BFA is also modified to be adaptive depending on the solution improvement instead of the preset maximum number of iterations. The proposed algorithm is validated using several test systems. The results are compared with those obtained by other algorithms previously applied to solve the problem considering valve-point effects and power losses in addition to wind power.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".