Fractional-Sea Lion Optimization Based Routing and Charge Scheduling in Internet of Electric Vehicles
Why this work is in the frame
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Bibliographic record
Abstract
The increasing impact of emissions from fuel vehicles accounted to mitigate the emissions worldwide. This study develops a multi-objective model for charge scheduling in the Internet of Electric Vehicles (IoEV). The objective is to create a method for charging EVs in the IoEV network that is energy conscious. Here, the position of the charge station and the location of the EV are used to simulate the IoEV network. Following network simulation, charging planning is completed. First, the proposed Fractional-based Sea lion optimization algorithm (Fractional-SLO), which was developed by combining Fractional calculus (FC) with Sea Lion Optimization (SLO), is used to choose the path. Distance and energy are used to calculate one’s aptitude for selecting a path. The proposed Fractional-SLO algorithm is then used to schedule charges after that. It is now possible to model the fitness for charge scheduling using delay and energy cost. The proposed Fractional-SLO promised improved performance with a 0.279-min delay and a 20.337-km distance. When 50 vehicles are involved, the proposed method produces delays that are, respectively, 60.21%, 64.87%, 14.69%, and 17.56% smaller than those of the existing methods, namely MDP, Joint EV Routing and Charging Discharge Scheduling Strategy, and Aggregate Cost Perspective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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