|Table of Contents|

 Li Sun and Deyu Wang.Optimal Structural Design of the Midship of a VLCC Based on the Strategy Integrating SVM and GA[J].Journal of Marine Science and Application,2012,(1):59-67.[doi:10.1007/s11804-012-1106-5]
Click and Copy

Optimal Structural Design of the Midship of a VLCC Based on the Strategy Integrating SVM and GA


Optimal Structural Design of the Midship of a VLCC Based on the Strategy Integrating SVM and GA
Li Sun and Deyu Wang
Li Sun and Deyu Wang
State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
very large crude carrier (VLCC) structural scantlings structural optimization metamodel support vector machine (SVM) genetic algorithms (GA) double-hull oil tanker common structural rules (CSR)
In this paper a hybrid process of modeling and optimization, which integrates a support vector machine (SVM) and genetic algorithm (GA), was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier (VLCC) ship was carried out according to the direct strength assessment method in common structural rules (CSR), which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.


Andric J, ?ani? V (2010). The global structural response model for multi-deck ships in concept design phase. Ocean Engineering, 37(8-9), 688-704.
Arai M, Shimizu T, Suzuki T (2000). Optimization of transverse bulkhead design by response surface methodology. Journal of the Kansai Society of Naval Architects of Japan, 234, 237-244.
Chang Chihchung, Lin Chinjen (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27:1-27:27.
Desai KM, Survase SA, Saudagar PS, Lele SS, Singhal RS (2008). Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan. Biochemical Engineering Journal, 41(3), 266-273.
Gou Peng, Liu Wei, Cui Weicheng. (2007). A comparison of approximation methods for multidisciplinary design optimization of ship structures. Journal of Ship Mechanics, 11(6), 913-923.
Haykin SS (1999). Neural networks: a comprehensive foundation. Prentice Hall, 72-339.
Hughes OF (1983). Ship structural design: a rationally-based, computer-aided, optimization approach. Wiley, New York, 368-389.
Hughes OF, Mistree F, ?ani? V (1980). A practical method for the rational design of ship structures. Journal of Ship Research, 24(2), 101-113.
Jones DR, Schonlau M, Welch WJ (1998). Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4), 455-492.
Li Weihong, Liu Lijuan, Gong Weiguo (2010). Multi-objective uniform design as a SVM model selection tool for face recognition. Expert Systems with Applications, 38(6), 6689-6695.
Liu Xu, Lu Wencong , Jin Shengli, Li Yawei, Chen Nianyi (2006). Support vector regression applied to materials optimization of sialon ceramics. Chemometrics and Intelligent Laboratory Systems, 82(1-2), 8-14.
Nandi S, Badhe Y, Lonari J, Sridevi U, Rao BS, Tambe SS (2004). Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst. Chemical Engineering Journal, 97(2-3), 115-129.
Satish Kumar YV, Mukhopadhyay M (2000). Finite element analysis of ship structures using a new stiffened plate element. Applied Ocean Research, 22(6), 361-374.
Shao Xiongfei, Yu Minghua, Guo Xiaodong (2008). Structure optimization for very large oil cargo tanks based on FEM. Ship Building of China, 49(2), 41-51.
Suzuki T, Arai M, Shimizu T (2000). On the application of response surface methodology to the optimization of ship structural design. Journal of the Society of Naval Architects of Japan, 188, 545-557.
Vapnik VN (1998). Statistical learning theory. Wiley-Interscience, New York, 401-570.
Supported by the Project of Ministry of Education and Finance (No.200512) and the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10).Vapnik VN (2000). The nature of statistical learning theory. Springer Verlag, New York, 93-224.


Supported by the Project of Ministry of Education and Finance (No.200512) and the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10).
Last Update: 2012-03-16