Benchmarking multidisciplinary design optimization algorithms. A comparison of algorithms for multidisciplinary design optimization (MDO) is performed with the aid of a new software framework. This framework, pyMDO, was developed in Python and is shown to be an excellent platform for comparing the performance of the various MDO methods. pyMDO eliminates the need for reformulation when solving a given problem using different MDO methods: once a problem has been described, it can automatically be cast into any method. In addition, the modular design of pyMDO allows rapid development and benchmarking of new methods. Results generated from this study provide a strong foundation for identifying the performance trends of various methods with several types of problems.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 16 articles , 1 standard article )

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  1. Garcia, D.; Ghommem, M.; Collier, N.; Varga, B. O. N.; Calo, V. M.: PyFly: a fast, portable aerodynamics simulator (2018)
  2. Huang, Z. L.; Zhou, Y. S.; Jiang, C.; Zheng, J.; Han, X.: Reliability-based multidisciplinary design optimization using incremental shifting vector strategy and its application in electronic product design (2018)
  3. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  4. Kurdi, Mohammad: A structural optimization framework for multidisciplinary design (2015)
  5. Du, Dawei; Simon, Dan: Complex system optimization using biogeography-based optimization (2013)
  6. Balesdent, Mathieu; Bérend, Nicolas; Dépincé, Philippe; Chriette, Abdelhamid: A survey of multidisciplinary design optimization methods in launch vehicle design (2012)
  7. Lambe, Andrew B.; Martins, Joaquim R. R. A.: Extensions to the design structure matrix for the description of multidisciplinary design, analysis, and optimization processes (2012)
  8. Perez, Ruben E.; Jansen, Peter W.; Martins, Joaquim R. R. A.: PyOpt: a python-based object-oriented framework for nonlinear constrained optimization (2012)
  9. Yao, Wen; Chen, Xiaoqian; Ouyang, Qi; Van Tooren, Michel: A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization (2012) ioport
  10. Kennedy, Graeme J.; Hansen, Jorn S.: The hybrid-adjoint method: a semi-analytic gradient evaluation technique applied to composite cure cycle optimization (2010)
  11. Sitaraman, Jayanarayanan; Floros, Matthew; Wissink, Andrew; Potsdam, Mark: Parallel domain connectivity algorithm for unsteady flow computations using overlapping and adaptive grids (2010)
  12. Tedford, Nathan P.; Martins, Joaquim R. R. A.: Benchmarking multidisciplinary design optimization algorithms (2010)
  13. Tosserams, S.; Hofkamp, A. T.; Etman, L. F. P.; Rooda, J. E.: A specification language for problem partitioning in decomposition-based design optimization (2010) ioport
  14. Chittick, Ian R.; Martins, Joaquim R. R. A.: An asymmetric suboptimization approach to aerostructural optimization (2009)
  15. Martins, Joaquim R. R. A.; Marriage, Christopher; Tedford, Nathan: pyMDO: an object-oriented framework for multidisciplinary design optimization (2009)
  16. Spiteri, Raymond J.; Ter, Thian-Peng: pythNon: A PSE for the numerical solution of nonlinear algebraic equations (2008)