Comparison of metaheuristics for the problem of electric vehicle optimal charging and discharging in a smart parking lot
Bibliographic record
Abstract
This paper presents a metaheuristic-based approach to the problem of charging, discharging, and scheduling for electric vehicles (EVs) in a smart parking lot. EVs are becoming more and more popular and create a significant load on the power grid. With variable electricity rates, it is possible to control and optimize the charging and discharging of EVs in order to minimize the burden on the grid and the overall cost. This paper proposes a new solution encoding and fitness function to be used with metaheuristics for this problem. It compares the efficiency of seven metaheuristics, namely the genetic algorithm (GA), the particle swarm optimization (PSO), the grey wolf optimizer (GWO), the Coyote Optimization Algorithm (COA), the Equilibrium Optimizer (EO), the War Strategy Optimization (WSO), and the Competition of tribes and cooperation of members algorithm (CTCM). Results show that the best metaheuristic for the problem is not the most recent one but the GA, which is the oldest of the algorithms used in the comparison.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".