Comparison of metaheuristic algorithms and constraint handling approaches for multi-objective optimization of a tanker
Bibliographic record
Abstract
Optimization of marine structures involves many nonlinear constraints safeguarding against various limit states. Metaheuristic optimization algorithms are often the preferred choice for optimization, since they can handle such constraints, conflicting objectives and discrete design variables. Yet, their constraint handling technique (CHT) can significantly affect their performance. This article demonstrates the effect of constraint handling on the performance of a few prominent swarm and evolutionary algorithms. Beside genetic algorithms, a few recent swarm optimization algorithms are tested. Constraint handling is performed using a few prominent approaches and a few recently proposed techniques. Case study is a 180 m long chemical tanker which needs to fulfil class society’s requirements. Main frame is optimized considering structural weight and deck adequacy as two concurrent objectives. Optimization of deck adequacy leads to decrease of stresses in the deck. All algorithms are run for 30 times and the statistical results are presented. Results are compared in terms of objective values and speed of convergence. Results reveal that recently proposed swarm algorithms perform worse than well-known evolutionary algorithms in terms of the convergence rate and spread of the non-dominated front. In addition, algorithms’ performance is strongly influenced by the constraint handling approach used. Among the ones tested, the best CHT is the repair method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".