Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering ConferenceShanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

An Automatic Hull Form Design Optimization Method Applying MLP Approximation Model based on
Bayesian Hyperparameter Tuning
Yi Zhang2,3, Ning Ma1,2,3, Xiechong Gu1,2,3, QiQi Shi1,2,3
1State Key Laboratory of Ocean Engineering, Shanghai, China
2School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China

3. Institute of Marine Equipment, Shanghai Jiao Tong University, Shanghai, China;

to reduce resistance is rapidly growing interest in lines optimization(Campana et al. 2006; Ginnis et al, 2015; Cheng et al, 2018). Fig.1 shows
the automatic optimization loop which integrates a parametric modeler,a solver and optimization algorithm. In the process of optimization, thesolver performs huge calculations, which are often time-consuming.
Therefore, the surrogate model composed of a small amount of data canbe used to replace the solver to improve the efficiency.

ABSTRACTThis paper introduces a method of using Bayesian Optimization to build
an approximate model called Multilayer Perceptron(MLP) in the process
of automatic ship hull form optimization. An automatic optimization
procedure for ship hull integrates Computer Fluid Dynamics(CFD)
solver, ship hull modification tool, optimization algorithm andapproximation model. RBF (Radial Basis Function) interpolation is built
as a modification tool to modify hull shape. Sensitivity analysis isconducted to the selection of control points. The Multi-island Genetic
Algorithm (MIGA) is applied to the optimization. The MLP regression
is built as an approximation model to improve the efficiency of theoptimization for solving computationally expensive numericalsimulations. MLP model has many parameters called hyperparameters,
which largely determine the accuracy of the model. The paper presents a
training method called Bayesian tuning to obtain these parameters. By
training the data collected through Latin Hypercube sampling, the MLP
model composed of optimized hyperparameters has higher regression
accuracy. The KRISO Container Ship(KCS) has been used as averification model. As a result, the total resistance of KCS at service
speed is reduced by 1.8%. The result shows that this procedure has better
effectiveness and less time consumption in the ship hull optimization andthe optimized hull form applying this method have lower resistance thanthe original one.

Fig. 1 The optimization procedure

With the rapid development of CFD technology, a series of CFD solvers
have been developed, which has high precision. However, it still takes
several hours to evaluate the resistance performance of a ship. For
thousands of calculation examples in the optimization process, this kindof time consumption is undoubtedly unacceptable. With the rapid
development of data science, it is possible to predict hydrodynamic
performance based on approximate model with certain accuracy
guarantee (Boursier et al, 2018; Du and Zhu, 2018; Du et al, 2018).
Approximate models have been studied for a long time and the classicalmodels are RSM (Response Surface Method) model (Serani et al, 2016;Liu et al, 2017), RBF (Radial Basis Function) model (Forrester andKeane, 2009) and Kriging model (Mukhopadhyay et al, 2017). However,
it is impossible to use one surrogate modeling method to provide accurate
predictions for all types of problems (Ben and Tomaso, 2018). Becauseof its strong nonlinear representation, generalization ability and powerfulability, the approximation model based on machine learning algorithms(MLAs) becomes more and more popular in the fast performance
prediction (Couckuyt et al, 2011; Nie et al, 2021). One possible solution
is the application of so-called MLP (Multi-layer perceptron) neuralnetworks (Rynkiewicz, 2019) as surrogates for computationallyexpensive numerical simulations.

KEY WORDS: Ship hull optimization; MLP approximation model;
hyperparameters optimization; Bayesian tuning
INTRODUCTION
Ship line design is a complex and comprehensive technology, which
involves many technical fields and has strong constraints. It is the basis
premise and core link in the general design of ships. It has a decisive
impact on the navigation performance. However, the traditional line
design method strongly depends on the designer's experience, andconsumes a lot of model test and calculation resources. Therefore, it isurgent to develop automatic optimization without manual participation.
SBD(Simulation Based Design) technology opens up a new situation forship line design. This technology can help design engineers to explore
the design space under constraints and automatically get the optimal line
design plan. A large number of studies show that using SBD technology

Typically, an MLA regression model contains model parameters and3606