Multi-objective Ant Lion Optimizer for Optimal Distribution Network Reconfiguration
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".