SpringerLink
Log in

Decomposition method of complex optimization model based on global sensitivity analysis

  • Published:
Chinese Journal of Mechanical Engineering Submit manuscript

Abstract

The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. KROO I, ALTUS S, BRAUN R, et al. Multidisciplinary optimization methods for aircraft preliminary design[R]. Washington, D.C., USA: NASA Langley Technical Report Server, 1994: 697–707.

    Google Scholar 

  2. MA Mingxu, WANG Chengen, ZHANG Jiayi, et al. Multidisciplinary design optimization for complex product review[J]. Chinese Journal of Mechanical Engineering, 2008, 44(6): 16–20. (in Chinese)

    Google Scholar 

  3. NATALIA M, ALEXANDROV N M, HUSSAINI M Y. Multidisciplinary design optimization: state of the art[M]. Hampton, Virginia, ICASE/NASA Langley workshop on MDO, 1995: 90–97.

    Google Scholar 

  4. KODIYALAM S, SOBIESKI J S. Multidisciplinary design optimization-some formal methods, framework, requirements, and application to vehicle design[J]. International Journal of Vehicle Design, 2001, 25(1): 3–22.

    Article  Google Scholar 

  5. SOBIESKI J S. Multidisciplinary aerospace design optimization: survey of recent developments[J]. Structural Optimization, 1997, 14(1): 1–23.

    Article  Google Scholar 

  6. KIM H M, KOKKOLARAS M, LOUCA L S, et al. Target cascading in vehicle redesign: A class VI truck study[J]. International Journal of Vehicle Design, 2002, 29(3): 199–225.

    Article  Google Scholar 

  7. SOBIESKI J S, JAROSLAW T. MDO can help resolve the designer’s dilemma[J]. Aerospace America, 1991, 29(9): 32–35.

    Google Scholar 

  8. TAPPETA R V, RENAUD J E. Multi-objective collaborative optimization[J]. Journal of Mechanical Design, 1997, 119(3): 403–410.

    Article  Google Scholar 

  9. WUJEK B A, RENAUD J E, BATILL S M. Concurrent subspace optimization using design variable sharing in a distributed computing environment[J]. Concurrent Engineering: Research and Application, 1996, 4(4): 361–275.

    Article  Google Scholar 

  10. BRAUN R D, KROO I M. Development and application of the Collaborative optimization architecture in a multidisciplinary design environment[R]. Philadelphia, NASA Langley Technical Report Server, 1997: 98–116.

    Google Scholar 

  11. BRAUN R D, KROO I. Use of the collaborative optimization architecture for launch vehicle design[C]//6th AIAA/USAF/NASA/ ISSMO Symposium on Multidisciplinary Analysis and Optimization, Washington, Sept. 4–6, 1996: 306–318.

  12. KROO I, MANNING V. Collaborative optimization: Status and directions[C]//8th AIAA/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Long Beach CA, Sept. 6–8, 2000: 2000–2017.

  13. SOBIESKI J S, AGTE J S, SANDUSKY R R. Bi-level integrated system synthesis (BLISS)[R]. Hampton, Virginia, NASA Langley Research Center, 1998.

    Google Scholar 

  14. ZHANG **g, LI Bailin, LIU Yongjun. Decoupling method of multidisciplinary design optimization based on sensitivity analysis[J]. Journal of Southwest JiaoTong University, 2007, 42(5): 563–567. (in Chinese)

    Google Scholar 

  15. TONG Lingsheng, SHI Boqiang, SHEN Yanhua, et al. First-order reliability method based multidisciplinary design optimization on gear transmission[J]. Chinese Journal of Mechanical Engineering, 2010, 46(3): 42–46. (in Chinese)

    Article  Google Scholar 

  16. MICHELENA N, KIM H M, PAPALAMBROS P. A system partitioning and optimization approach to target cascading[J]. International Conference on Engineering Design, 1999, 26(24): 1109–1112.

    Google Scholar 

  17. MICHELENA N, Park H, PAPALAMBROS P. Convergence properties of analytical target cascading[J]. American Institute of Aeronautics and Astronautics Journal, 2003, 41(5): 897–905.

    Article  Google Scholar 

  18. LIU Huibin, CHEN Wei. Probabilistic analytical target cascading: A moment matching formulation for multilevel optimization under uncertainty[J]. Journal of Mechanical Design, 2006, 128(6): 991–1011.

    Article  Google Scholar 

  19. LI Yan**g, LU Zhaosong, MICHALEK J J. Diagonal quadratic approximation for parallelization of analytical target cascading[J]. Journal of Mechanical Design, 2008, 130(5): 51402–05140210.

    Article  Google Scholar 

  20. CHU Xuezheng. Research on design space exploration and coordinated decomposition methods for complex product[D]. Wuhan: Huazhong University of Science and Technology, 2010. (in Chinese)

    Google Scholar 

  21. SOBOL’ I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates[J]. Mathematics and Computers in Simulation, 2001, 55(3): 271–280.

    Article  MathSciNet  MATH  Google Scholar 

  22. TOSHIMITSU H, SALTELLI A. Importance measure in global sensitivity analysis of nonlinear models[J]. Reliability Engineering and System Safety, 1996, 52(1): 1–17.

    Article  Google Scholar 

  23. DENG Zhaoxiang, YAN Changzheng. Sensitivity analysis of a motorcycle frame using the Monte Carlo method[J]. Journal of Chongqing University, 2008, 31(10): 1113–1122. (in Chinese)

    Google Scholar 

  24. KODIYALAM S. Evaluation of methods for multidisciplinary design optimization (MDO) Phase 1[R]. Washington: National Aeronautics and Space Administration, 1998: 23–26.

    Google Scholar 

  25. XUE Caijun, NIE Hong, QIU Qingying, et al. A peer-to-peer distributed collaborative optimization system[J]. Scientific Programming, 2004, 12(2): 121–131.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, 310027, China

    Qingying Qiu, Bing Li, Peien Feng & Yu Gao

Authors
  1. Qingying Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peien Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qingying Qiu.

Additional information

Supported by National Natural Science Foundation of China (Grant No. 51075356), and National Key Technology R&D Program (Grant No. 2013BAF07B04)

QIU Qingying, born in 1970, is currently an associate professor at Zhejiang University, China. His research interests include innovative design and optimization design.

LI Bing, born in 1984, is currently a PhD candidate at Zhejiang University, China. His research interests include multidisciplinary design optimization.

FENG Peien, born in 1943, is currently a professor and a PhD candidate supervisor at Zhejiang University, China. His research interests include mechanical design theory and methodology.

GAO Yu, born in 1973, is currently a lab technician at Zhejiang University, China. His research interests include construction machinery intelligent control and energy-saving.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qiu, Q., Li, B., Feng, P. et al. Decomposition method of complex optimization model based on global sensitivity analysis. Chin. J. Mech. Eng. 27, 722–729 (2014). https://doi.org/10.3901/CJME.2014.0516.096

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3901/CJME.2014.0516.096

Keywords

Search

Navigation