Multi-Objective Optimization of Vehicle Passive Suspension System Using NSGA-II, SPEA2 and PESA-II
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Bibliographic record
Abstract
This paper presents a constrained multi-objective optimization study of vehicle passive suspension system which is modeled as a passive half car ride model. For multi-objective optimization the most widely used multi-objective evolutionary algorithms such as NSGA-II, SPEA2 and PESA-II are employed. The potential of the MOEAs in obtaining the better Pareto front of optimal solutions and in maintaining the diversity among the optimal solutions is tested by conducting 2 and 3-objective optimization studies. The results show that NSGA-II is able to yield a better Pareto front in terms of minimizing the objective vector but SPEA2 and PESA-II has a better diversified set of optimal solutions. Overall, all three algorithms have performed equally in optimizing the problem with the nature of the equations is second order ordinary differential equations.
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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