1994
DOI: 10.1016/0307-904x(94)90307-7
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A dynamic penalty function method for the solution of structural optimization problems

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Cited by 72 publications
(38 citation statements)
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“…The optimization method used in this study is the DY-NAMIC-Q method of Snyman et al 5) This approach involves the application of a dynamic trajectory method for unconstrained optimization, 10,11) adapted to handle constrained problems through appropriate penalty function formulations. 12,13) This DYNAMIC method is applied to successive approximate Quadratic subproblems 6,7) of the original problem (here problems (1 and 2)).…”
Section: Optimization Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…The optimization method used in this study is the DY-NAMIC-Q method of Snyman et al 5) This approach involves the application of a dynamic trajectory method for unconstrained optimization, 10,11) adapted to handle constrained problems through appropriate penalty function formulations. 12,13) This DYNAMIC method is applied to successive approximate Quadratic subproblems 6,7) of the original problem (here problems (1 and 2)).…”
Section: Optimization Algorithmmentioning
confidence: 99%
“…In contrast, the current study uses CFD combined with mathematical optimization to perform a systematic design optimization process for tundish furniture design. This is achieved in an automatic fashion in this study, by linking the commercial CFD code FLUENT 3) and its pre-processor GAMBIT 4) to the DYNAMIC-Q algorithm of Snyman et al, 5) in which a gradient method for constrained optimization is applied to successive approximate quadratic subproblems. 6,7) When injecting a pulse of tracer element at the shroud (inlet) of a tundish, this tracer is detected at the Submerged Entry Nozzle (SEN) (outlet of the tundish) after a certain amount of time.…”
Section: Introductionmentioning
confidence: 99%
“…The single-objective nonlinear optimization problems are able to be solved well by combining the penalty function's ability to handle constraints with the optimal performance of DE_CMSBHS algorithm [13,14]. The optimization problem of hybrid A F B 0 D S laminates with immunity to HTSD is a typical nonlinear mixed integer-real single-objective optimization problem.…”
Section: Introductionmentioning
confidence: 99%
“…Each objective function evaluation is the solution of a CFD simulation. Many optimisation algorithms can be found that have been developed to deal with the difficulties mentioned above, some such algorithms are presented in Ref [58][59][60][61][62][63] Unfortunately there are a limited number of optimisation algorithms available in the SciPy python library when considering multiple design variables and constraint functions. The objective function is thus minimised using the Sequential Least SQuares Programming (SLSQP) algorithm available in the SciPy python library "fmin slsqp" [53].…”
Section: Numerical Optimisation Algorithmmentioning
confidence: 99%