Trust region model management in multidisciplinary design optimization

Rodrı́guez, José F.; Renaud, John E.; Wujek, Brett; Tappeta, Ravindra V.

doi:10.1016/s0377-0427(00)00424-6

Cited by 66 publications

(28 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Antoniou & W.-S. Lu (2007)); nonlinear interior point methods (see A. Antoniou & W.-S. Lu (2007);El-Barky et al (1996)), and; response surface approximation methods (RSM) (see Rodríguez et al (2000); Wang (2001)). Local methods are prone to finding an optimum in the nearby region of the initial starting guess; however, these methods work very efficiently in the vicinity of the optimum.…”

Section: Constraint Optimization Algorithmsmentioning

confidence: 99%

Rotor Design for High-Speed Flywheel Energy Storage Systems

Krack¹,

Secanell²,

Mertiny³

2011

Energy Storage in the Emerging Era of Smart Grids

View full text Add to dashboard Cite

Section: Constraint Optimization Algorithmsmentioning

confidence: 99%

Rotor Design for High-Speed Flywheel Energy Storage Systems

Krack¹,

Secanell²,

Mertiny³

2011

Energy Storage in the Emerging Era of Smart Grids

View full text Add to dashboard Cite

“…Sobieski and Kroo (2000) use response surface models within a collaborative optimization framework. Several sequential approximate optimization strategies using local response surface models and move limits have been recently developed (Alexandrov and Lewis 2001;Rodriguez et al 2000Rodriguez et al , 2001Sobester et al 2004). These strategies are primarily developed for the optimization of single objective problems.…”

Section: Existing Metamodeling Approachesmentioning

confidence: 99%

“…Research in response surface-based methods has largely focused on handling single responses. A major drawback of several response surface frameworks proposed in the literature (Alexandrov and Lewis 2001;Rodriguez et al 2000Rodriguez et al , 2001Sobester et al 2004) is the lack of an effective procedure to extend the strategies to multiobjective optimization, which would exploit the potential computational opportunities. Nevertheless, there does exist a simple and intuitive approach: treating each design objective and design constraint independently, and constructing metamodels over most of the design space.…”

Section: Metamodeling For Multiple Objectives-limitations Of Existingmentioning

confidence: 99%

A computationally efficient metamodeling approach for expensive multiobjective optimization

Messac

Mullur

2007

Optim Eng

View full text Add to dashboard Cite

This paper explores a new metamodeling framework that may collapse the computational explosion that characterizes the modeling of complex systems under a multiobjective and/or multidisciplinary setting. Under the new framework, a pseudo response surface is constructed for each design objective for each discipline. This pseudo response surface has the unique property of being highly accurate in Pareto optimal regions, while it is intentionally allowed to be inaccurate in other regions. In short, the response surface for each design objective is accurate only where it matters. Because the pseudo response surface is allowed to be inaccurate in other regions of the design space, the computational cost of constructing it is dramatically reduced. An important distinguishing feature of the new framework is that the response surfaces for all the design objectives are constructed simultaneously in a mutually dependent fashion, in a way that identifies Pareto regions for the multiobjective problem. The new framework supports the puzzling notion that it is possible to obtain more accuracy and radically more design space exploration capability, while actually reducing the computation effort. This counterintuitive metamodeling paradigm shift holds the potential for identifying highly competitive products and systems that are well beyond today's state of the art.

show abstract

“…Robust global behaviour infers the mathematical assurance that the optimization algorithm will converge to an initial iterate [7,8]. In trust region methods, a secondorder approximation,f (x), of the objective, f(x), is successively minimized with the trust region regulating the length of the steps in each iteration.…”

Section: Trust Region Modes Management Strategiesmentioning

confidence: 99%

Determining the optimum variable blank-holder forces using adaptive response surface methodology (ARSM)

Sun

Chen

Lin

2004

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Wrinkling and fracture are main defects in sheetmetal forming. They can be reduced or eliminated by manipulating a suitable blank-holder force (BHF). But it is difficult to attain the optimum BHF during sheet-metal forming. In this article, a new optimization algorithm integrating the finite element method (FEM) and adaptive response surface methodology £(r)ARSM£(c) is presented to determinate the optimum BHF. To assure convergence, the trust region modes management strategies are used to adjust the move limit of design spaces. Finally, the optimum results of rectangular box deep drawing are given. Experiments are performed to verify the optimal result.

show abstract

Trust region model management in multidisciplinary design optimization

Cited by 66 publications

References 18 publications

Rotor Design for High-Speed Flywheel Energy Storage Systems

Rotor Design for High-Speed Flywheel Energy Storage Systems

A computationally efficient metamodeling approach for expensive multiobjective optimization

Determining the optimum variable blank-holder forces using adaptive response surface methodology (ARSM)

Contact Info

Product

Resources

About