Improved multi-objective structural optimization with adaptive repair-based constraint handling
Why this work is in the frame
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Bibliographic record
Abstract
Engineering optimization typically involves a large number of nonlinear constraints; therefore, effective constraint handling techniques (CHTs) are sought for metaheuristic optimization algorithms. Modified repair-based CHT is proposed here for a multi-objective evolutionary algorithm based on decomposition (MOEA/D). This CHT is: (1) adaptive to the share of infeasible solutions in a population; (2) free of problem-specific heuristics that users typically need to provide for repair; and (3) without control parameters. Infeasible solutions with superior decomposition function value are repaired using information contained in the neighbourhoods of the current population. The approach is tested on four multi-objective problems: a common mathematical optimization benchmark problem, two truss optimization problems and a real-world structural design of a tanker ship. A few prominent CHTs and metaheuristic algorithms are used for comparison. With the proposed CHT, MOEA/D shows improved convergence speed and spread of the Pareto front, providing competitive results in comparison to the other algorithms.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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