Electric Vehicle Parking Lot Scheduling Using Parallel Genetic Algorithm on a Graphics Processing Unit
Bibliographic record
Abstract
Electric Vehicles (EV) are increasingly popular due to environmental benefits but can strain the power grid. Optimizing EV charging in large parking lots is a challenge. This study uses a metaheuristic approach on a graphics processing unit (GPU) to schedule EV charging in a smart parking lot. The proposed method uses a genetic algorithm (GA) to create an efficient charging schedule that minimizes cost and respects charger and feeder capacities. The algorithm leverages parallelism in GPU. Tested on lots with different vehicle capacities, the proposed GA-based method is compared to a particle swarm optimization (PSO) method and achieve better results. The GPU implementation speeds up the calculation by 134.5x compared to a sequential execution on CPU. It can optimize a 20-EV lot in 0.46 seconds and a 500-EV lot in 5.80 seconds, allowing for real-time computation.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".