Fractional-Sea Lion Optimization Based Routing and Charge Scheduling in Internet of Electric Vehicles
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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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".