An improved support vector regression and its modelling of manoeuvring performance in multidisciplinary ship design optimization

Ship design is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional ship design process only involves independent design optimization with some regression formulas within each discipline and there is no guarantee to achieve the optimum design. At the same time, it is crucial to improve the efficiency of modern ship design. Nowadays, the methods of computational fluid dynamics (CFD) has been brought into the ship design optimization. However, there are still some problems such as calculation precision and time consumption especially when CFD software is inlaid into the optimization procedure. Modeling is a far-ranging and all-around subject, and its precision directly affects the scientific decision in future. How to establish an accurate approximation model instead of the CFD calculation will be the key problem. The Support Vector Machines (SVM), a new general machine learning method based on the frame of statistical learning theory, may solve the problems in sample space and be an effective method of processing the non-liner classification and regression. The classical SVR has two parameters to control the errors. A new algorithm of Support Vector Regression proposed in this article has only one parameter to control the errors, adds b2/2 to the item of confidence interval at the same time, and adopts the Laplace loss function. It is named Single-parameter Lagrangian Support Vector Regression (SPL-SVR). This effective algorithm can improve the operation speed of program to a certain extent, and has better fitting precision. In practical design of ship, Design of Experiment (DOE) and the proposed support vector regression algorithm are applied to ship design optimization to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. The result indicates that the SPL-SVR method to establish approximate models can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

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Support Vector Regression-Based Multidisciplinary Design Optimization for Ship Design

Volume 1: Offshore Technology ◽

10.1115/omae2012-83086 ◽

2012 ◽

Author(s):

Dongqin Li ◽

Yifeng Guan ◽

Qingfeng Wang ◽

Zhitong Chen

Keyword(s):

Design Optimization ◽

Support Vector Regression ◽

Design Process ◽

Multidisciplinary Design Optimization ◽

Initial Point ◽

Ship Design ◽

Collaborative Optimization ◽

Multidisciplinary Design ◽

Support Vector ◽

Independent Design

The design of ship is related to several disciplines such as hydrostatic, resistance, propulsion and economic. The traditional design process of ship only involves independent design optimization within each discipline. With such an approach, there is no guarantee to achieve the optimum design. And at the same time improving the efficiency of ship optimization is also crucial for modem ship design. In this paper, an introduction of both the traditional ship design process and the fundamentals of Multidisciplinary Design Optimization (MDO) theory are presented and a comparison between the two methods is carried out. As one of the most frequently applied MDO methods, Collaborative Optimization (CO) promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, Design Of Experiment (DOE) and a new support vector regression algorithm are applied to CO to construct statistical approximation model in this paper. The support vector regression algorithm approximates the optimization model and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method. Then this new Collaborative Optimization (CO) method using approximate technology is discussed in detail and applied in ship design which considers hydrostatic, propulsion, weight and volume, performance and cost. It indicates that CO method combined with approximate technology can effectively solve complex engineering design optimization problem. Finally, some suggestions on the future improvements are proposed.

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