Implementation of a fast non-dominated sorting firefly algorithm and a vehicle simulation model for multi-objective component sizing of a power-split PHEV powertrain: a comparative numerical study
Why this work is in the frame
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Bibliographic record
Abstract
In the current investigation, the authors take advantage of a well-known emerging swarm intelligence-based metaheuristic method, i.e., firefly algorithm (FA), to cope with a tedious automotive optimisation problem, known as component sizing. As far as the authors are concerned, the presented research can be considered as one of the rare archived reports which substantiate the applicability and efficacy of metaheuristics for the component sizing of power-split plug-in hybrid electric vehicle (PHEV) powertrains. Here, the authors take one further step and formulate a complex multiobjective optimisation problem to clearly investigate the potentials of metaheuristics. It is worth pointing out that most of the existing classical optimisation approaches are unable to successfully solve a multiobjective component sizing problem, and are often trapped into local minimums and offer local Pareto solutions. Moreover, through a numerical comparative study, the superiority of the proposed fast non-dominated sorting firefly algorithm (FNSFA) over the non-dominated sorting genetic algorithm (NSGA-II) is demonstrated. The outcomes of this research encourage automotive engineers to take advantage of nature-based optimisers, e.g., FNSFA, for component sizing problems.
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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 | 0.000 |
| 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