Optimal structural design of the midship of a VLCC based on the strategy integrating SVM and GA

Abstract

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.

Keywords

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)

References

[1] Andric J., Žanić V. The global structural response model for multi-deck ships in concept design phase. Ocean Engineering, 2010, 37(8–9): 688-704

[2] Arai M., Shimizu T., Suzuki T. Optimization of transverse bulkhead design by response surface methodology. Journal of the Kansai Society of Naval Architects of Japan, 2000, 234: 237-244

[3] Chang C., Lin C. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 27

[4] Desai K.M., Survase S.A., Saudagar P.S., Lele S.S., Singhal R.S. 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, 2008, 41(3): 266-273

[5] Gou P., Liu W., Cui Weicheng. A comparison of approximation methods for multidisciplinary design optimization of ship structures. Journal of Ship Mechanics, 2007, 11(6): 913-923

[6] Haykin SS (1999). Neural networks: a comprehensive foundation. Prentice Hall, 72–339.

[7] Hughes O.F. Ship structural design: a rationally-based, computer-aided, optimization approach, 1983, New York: Wiley, 368-389

[8] Hughes O.F., Mistree F., Žanić V. A practical method for the rational design of ship structures. Journal of Ship Research, 1980, 24(2): 101-113

[9] Jones D.R., Schonlau M., Welch W.J. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 1998, 13(4): 455-492

[10] Li W., Liu L., Gong W. Multi-objective uniform design as a SVM model selection tool for face recognition. Expert Systems with Applications, 2010, 38(6): 6689-6695

[11] Liu X., Lu W., Jin S., Li Y., Chen N. Support vector regression applied to materials optimization of sialon ceramics. Chemometrics and Intelligent Laboratory Systems, 2006, 82(1–2): 8-14

[12] Nandi S., Badhe Y., Lonari J., Sridevi U., Rao B.S., Tambe S.S. 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, 2004, 97(2–3): 115-129

[13] Satish Kumar Y.V., Mukhopadhyay M. Finite element analysis of ship structures using a new stiffened plate element. Applied Ocean Research, 2000, 22(6): 361-374

[14] Shao X., Yu M., Guo X. Structure optimization for very large oil cargo tanks based on FEM. Ship Building of China, 2008, 49(2): 41-51

[15] Suzuki T., Arai M., Shimizu T. On the application of response surface methodology to the optimization of ship structural design. Journal of the Society of Naval Architects of Japan, 2000, 188: 545-557

[16] Vapnik V.N. Statistical learning theory, 1998, New York: Wiley-Interscience, 401-570

[17] Vapnik V.N. The nature of statistical learning theory, 2000, New York: Springer Verlag, 93-224