Skip to main content
  • 693 Accesses

Abstract

Ship hull form optimization is a typical engineering optimization problem, involving a large number of design variables and constraints. In this optimization, the objective function and design variables are of hidden relationship due to its strong nonlinear phenomenon.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lin Y, Zhu Z, Ji Z, Dai Y (1997) Form description with function parameter [J]. Shipbuild China, 3:74–78

    Google Scholar 

  2. Jiang JS, He CX, Pan SH (2008). Optimization method [M]. South China University of Technology Press, Guangzhou

    Google Scholar 

  3. Xi Shao Lin, Zhao Feng Zhi (1983) Optimization method [M]. Shanghai Science and Technology Press, Shanghai

    Google Scholar 

  4. Xing Wen Xun, Xie Jin Xing (1999) Modern optimization method [M]. Tsinghua University Press, Beijing

    Google Scholar 

  5. Bei-yue Z, Guang-qi G (2001) Research of improving genetic algorithms based on niche technology [J]. J Yueyang Norm Univ (Natural Sciences), 14(4):18–21

    Google Scholar 

  6. Zhou GQ (1999) Practical discrete optimization approach for ship structures based on genetic algorithms [J]. Ship Technol Res, 46(3):179–188

    Google Scholar 

  7. Xuan GN, Cheng RW (2000) Genetic algorithm and engineering design [M]. Science Press, Beijing

    Google Scholar 

  8. Della Cioppa A, De Stefano C (2004) On the role of population size and niche radius in fitness sharing [J]. IEEE Trans Evol Comput 8(6):580–592

    Article  Google Scholar 

  9. Vieira DAG, Adriano RLS (2004) Treating constraints as objectives in mul- tiobjective optimization problems using niche pareto genetic algorithm [J]. IEEE Trans Magn 3(40):1188–1191

    Article  Google Scholar 

  10. Kim JK (2002) Niche genetic algorithm adopting restricted competition selection combined with pattern search method [J]. IEEE Trans Magn 38(2):1001–1004

    Article  Google Scholar 

  11. Zhou Y-J, Wang L-Z (2006) An application of elitist model niche genetic algorithms in optimization of functions [J]. J Liuzhou Teachers College, 21(1):107–110

    Google Scholar 

  12. Xin X-J1, 2, Pan J3, Xiao G-Z (2005) New proxy signature scheme based on Schnorr signature scheme [J]. J Chongqing Univ Posts Telecommunications (Natural Science), 17(6): 721–724

    Google Scholar 

  13. Pereira CM, Sacco WF (2008) A parallel genetic algorithm with niching technique applied to a nuclear reactor core design optimization problem [J]. Prog Nuclear Energy, 1–7

    Article  Google Scholar 

  14. Ke F, Li Y (2014) The forecasting method of landslides based on improved BP neural network [J]. Geotechnical Investigation Surveying, (8):55–60

    Google Scholar 

  15. Liu Y, Li R, Li C (2014) Application of BP neural network and support vector machine to the accumulated temperature interpolation [J]. J Arid Land Resources Environ 28(5):158–165

    Google Scholar 

  16. Gao Peng Yi (2012) Study on the optimization of backpropagation neural network classifier [D]. Huazhong University of Science and Technology, Wuhan

    Google Scholar 

  17. Wang HF, Zhao M, Meng G (2002) 3-D shape prediction and global approximate optimization of plastic ball grid array (PBGA) solder joints [J]. J Shanghai Jiaotong University, 36(6):829–833

    Google Scholar 

  18. Liu JH (2009) The research of basic theory and improvement on particle swarm optimization [D]. Central South University, Changsha

    Google Scholar 

  19. Li YL (2014) The study of particle swarm algorithm based on multi-objective optimization and its application [D]. Chengdu: Southwest Jiaotong University

    Google Scholar 

  20. Guang-yu G, Chen G, Li Y-X (2014) Fractional-order control of USV course based on improved PSO algorithm [J]. Syst Eng Electron 36(6):1146–1151

    Google Scholar 

  21. Lujia Z, Fang Z, Shan ZP (2001) The parameters of generator are identified by genetic algorithm and constrained variable scale optimization [J]. Hunan Electric Power, 21(3):5–7

    Google Scholar 

  22. Yin X (2015) Fundamental research on thermal reliability engineering design for cylinder head [D]. Zhejiang University, Zhejiang

    Google Scholar 

  23. Lai YY (2012) ISIGHT parameter optimization theory and examples [M]. Beijing University of Aeronautics and Astronautics Press, Beijing

    Google Scholar 

  24. Huang J-F, Wan S-L (2012) The parametric method of FRIENDSHIP hull form based on design feature [J]. Chin J Ship Res, 7(2):79–85

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Shanghai Jiao Tong University Press, Shanghai and Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhang, BJ., Zhang, SL. (2019). Optimization Method and Optimization Platform. In: Research on Ship Design and Optimization Based on Simulation-Based Design (SBD) Technique. Springer, Singapore. https://doi.org/10.1007/978-981-10-8423-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8423-2_4

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8422-5

  • Online ISBN: 978-981-10-8423-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics