Journal of Marine Science and Application

, Volume 11, Issue 1, pp 59–67 | Cite as

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

  • Li Sun
  • Deyu Wang
Research Papers


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.


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) 


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Copyright information

© Harbin Engineering University and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.State Key Laboratory of Ocean EngineeringShanghai Jiao Tong UniversityShanghaiChina

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