Novel constrained search-tactic for optimal dynamic economic dispatch using modern meta-heuristic optimization algorithms
Bibliographic record
Abstract
Meta-heuristic optimization algorithms have gained popularity in solving complex, constrained optimization problems. The dynamic economic dispatch (DED) problem represents an example of such complex, constrained optimization problems. The aim of DED is to operate online units economically to meet the load demand, subjected to satisfying highly nonlinear and non-convex practical constraints. Therefore, it is possible that computational methods may not yield a global extremum as many local extrema may be encountered. This paper presents a novel constrained search-tactic to solve the DED problem. Two recently introduced meta-heuristic techniques, namely sensory-deprived optimization algorithm (SDOA) and artificial bee colony (ABC) algorithm, are adopted to evaluate the performance of the proposed constraint search-tactic. Two test systems are used to reveal the effectiveness of the offered tactic which successfully accelerates the employed algorithms' performance toward the optimal feasible region. After comparing the results, the outcomes when integrating the constrained search-tactic either outperformed or matched those obtained using other well-known methods.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".