A ship type optimization method based on transfer learning

By employing Two-Stage TrAdaBoost.R2 transfer learning and dual-proxy model optimization algorithms, the problem of high-cost ship model testing in shipbuilding engineering was solved, achieving efficient and accurate ship hull optimization, reducing resource consumption, and improving ship performance.

CN116720260BActive Publication Date: 2026-08-25JIANGNAN UNIV +1
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Patent Information

Application Number
CN202310727001.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-08-10
Publication Date
2026-08-25
Estimated Expiration
2043-08-10

AI Technical Summary

Technical Problem

In shipbuilding engineering, traditional ship model testing methods are costly, and obtaining experimental or simulation data is time-consuming and resource-intensive, making it difficult to effectively optimize ship designs to improve performance and efficiency.

Method used

We adopted the Two-Stage TrAdaBoost.R2 transfer learning method, combined with a dual-agent model and an improved QPSO optimization algorithm, and used existing experimental data to assist in modeling. We then optimized the ship type through transfer learning when there was insufficient data in the target domain.

Benefits of technology

It reduces modeling costs, improves model accuracy and optimization speed, ensures the accuracy and reliability of optimization results, is applicable to the optimization of different ship performances, accelerates convergence speed, and reduces the probability of getting trapped in local optima.

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Abstract

The application discloses a ship type optimization method based on transfer learning, and relates to the technical field of ship type optimization.The application improves the precision and generalization performance of the final ship performance model by using a Two Stage TrAdaBoost.R2 regression transfer algorithm.The algorithm uses the knowledge of some known related data sets to assist the target ship type data modeling, so as to improve the precision of the model.The application is divided into two stages for ship type sampling and modeling: a coarse proxy model is constructed based on global scale sampling data, and a fine proxy model is constructed based on the data obtained through the global and local scale sampling stages, and the two models jointly assist the subsequent optimization process.A double proxy assisted optimization method is proposed for the optimization process.The iteration process of the adopted optimization algorithm is improved, and the single proxy model is no longer updated independently, but the fitness values of the two proxy models are considered simultaneously in the optimization process, and the optimal one is selected for iteration.
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