A Non-Linear-Threshold-Accepting Function Based Algorithm for the Solution of Economic Dispatch Problem
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Bibliographic record
Abstract
This article introduces a novel heuristic algorithm based on Non-Linear Threshold Accepting Function to solve the challenging non-convex economic dispatch problem. Economic dispatch is a power system management tool; it is used to allocate the total power generation to the generating units to meet the active load demand. The power systems are highly nonlinear due to the physical and operational constraints. The complexity of the resulting non-convex objective cost function led to inabilities to solve the problem by using analytical approaches, especially in the case of large-scale problems. Optimization techniques based on heuristics are used to overcome these difficulties. The Non-Linear Threshold Accepting Algorithm has demonstrated efficiency in solving various instances of static and dynamic allocation and scheduling problems but has never been applied to solve the economic dispatch problem. Existing benchmark systems are used to evaluate the performance of the proposed heuristic. Additional random instances with different sizes are generated to compare the adopted heuristic to the Harmony Search and the Whale Optimization Algorithms. The obtained results showed the superiority of the proposed algorithm in finding, for all considered instances, a high-quality solution in minimum computational time.
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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 | 0.000 |
| 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