Multi-objective Ant Lion Optimizer for Optimal Distribution Network Reconfiguration
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, the multi-objective distribution network reconfiguration (DNR) problem was addressed using a multi-objective Ant Lion Optimizer (MOALO) algorithm. The proposed approach was tested in the IEEE 33-bus system in order to simultaneously minimize active power losses and enhance reliability while taking into account a set of operational and topological constraints. To calculate power losses, the Backward/Forward algorithm was applied. Moreover, the union-find with path compression approach was used to keep radiality of each network configuration. To evaluate the performance of MOALO, it was benchmarked against the weighted-sum method (using Genetic Algorithm (GA)), Nondominated Sorting Genetic Algorithm II (NSGA-II), multiobjective Particle Swarm Optimization (MOPSO) and multiobjective Grey Wolf Optimizer (MOGWO). The research findings revealed that the proposed MOALO algorithm effectively reduces total power losses by up to $\mathbf{3 1. 1 4 \%}$ and the total ENS index by $33.35 \%$, while also providing a wellbalanced compromise solution with power losses around 141.92 kW and improved reliability with a total ENS index of approximately $5037 \mathrm{kWh} /$ year.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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