Structural Optimization of Ships: Benchmark Study of Metaheuristic Algorithms and Constraint Handling Approaches
Bibliographic record
Abstract
Abstract Optimization of ship structures can be performed using swarm and evolutionary algorithms in search for global optima, which is useful to maximize performance and minimize costs. There is a wide variety of such algorithms available and some have been proposed recently, but tested on mathematical problems. Thus, a benchmark study is performed here to assess their performance on structural optimization of a 180 m long chemical tanker which needs to fulfil class society’s requirements. Main frame is optimized considering firstly structural weight as a single objective and secondly weight and deck adequacy as two concurrent objectives. Optimization of deck adequacy leads to decrease of stresses in the deck. Artificial neural network is used as surrogate model to reduce the computational time. Following swarm and evolutionary algorithms are considered: PSO, NSGA-II, MOEA/D, MVO, MOMVO, MOGWO, IGWO and GSA. Following constraint handling techniques (CHTs) are used within: static and dynamic penalty, adaptive threshold and repair method. 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 CHT 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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".