Target Cascading in Optimal System Design

Kim, Hyung Min; Michelena, Nestor; Papalambros, Panos Y.; Jiang, Tao

doi:10.1115/1.1582501

Cited by 443 publications

(280 citation statements)

References 12 publications

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“…The analytical target cascading (ATC) formulation is another example in this category, but now formulated for an arbitrary number of levels (Michelena et al 1999;Kim 2001;Kokkolaras et al 2002;Kim et al 2003;Michelena et al 2003). Various penalty functions φ j have been proposed for ATC (see Michelena et al 2003;Kim et al 2006;Tosserams et al 2006), and a comparison can be found in Li et al (2008).…”

Section: Closed Design Open Consistencymentioning

confidence: 99%

A classification of methods for distributed system optimization based on formulation structure

Tosserams

Etman

Rooda

2009

Struct Multidisc Optim

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This paper presents a classification of formulations for distributed system optimization based on formulation structure. Two main classes are identified: nested formulations and alternating formulations. Nested formulations are bilevel programming problems where optimization subproblems are nested in the functions of a coordinating master problem. Alternating formulations iterate between solving a master problem and disciplinary subproblems in a sequential scheme. Methods included in the former class are collaborative optimization and BLISS2000. The latter class includes concurrent subspace optimization, analytical target cascading, and augmented Lagrangian coordination. Although the distinction between nested and alternating formulations has not been made in earlier comparisons, it plays a crucial role in the theoretical and computational properties of distributed optimization methods. The most prominent general characteristics for each class are discussed in more detail, providing valuable insights for the theoretical analysis and further development of distributed optimization methods.

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Section: Closed Design Open Consistencymentioning

confidence: 99%

A classification of methods for distributed system optimization based on formulation structure

Tosserams

Etman

Rooda

2009

Struct Multidisc Optim

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“…Other common relaxation formulations involve penalty functions 39 or an augmented Lagrangian 10 function. Each decomposition formulation can be subdivided into:…”

Section: Iiia Physical Couplingmentioning

confidence: 99%

“…Sobieszczanski-Sobieski et al (1987), 76 Alexandrov and Dennis (1994), 1 Kim et al (2003), 41 amongst others) and multi-disciplinary optimization (e.g. Braun and Kroo (1997), 22 Rodriguez et al (2000), 57 Sobieszczanski-Sobieski et al (2000), 71,72 amongst others) techniques have been developed.…”

Section: Introductionmentioning

confidence: 99%

Overview of Methods for Multi-Level and/or Multi-Disciplinary Optimization

Wit¹,

Keulen²

2010

51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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5
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Multi-level optimization and multi-disciplinary optimization are areas of research that are concerned with developing efficient analysis and optimization techniques for complex systems that are made up of coupled elements (components). Within the field of multilevel optimization and multi-disciplinary optimization a large number of techniques have been developed for efficient analysis and optimization of complex systems. This paper presents an unified overview of main stream approaches that were found in the literature. Four general steps are distinguished in both multi-level optimization and multi-disciplinary optimization: physical coupling, optimization problem coupling, coordination and solution sequence. Via these four steps approaches are classified and possibilities for combining aspects of different methods are given. Finally, advantages and disadvantages of approaches applied to engineering problems are discussed and directions for further research are given.

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“…This technique then attempts to minimize deviations between design targets and subsystem responses to achieve an optimal solution for the system [21,22].…”

Section: Review Of Analytical Target Cascadingmentioning
confidence: 99%

Reduced representations of vector-valued coupling variables in decomposition-based design optimization
Alexander
¹
,
Allison
²
,
Papalambros
³

2011
Struct Multidisc Optim
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17
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Decomposition-based methods for system design optimization introduce consistency constraints, which contain coupling variables communicated between adjacent subproblems and link them together. When these variables are vector-valued (e.g., dynamic responses), the problem size can increase dramatically and make such methods impractical. Therefore, it is necessary to represent these vector-valued coupling variables with a reduced representation that will enable efficient optimization while maintaining an acceptable level of accuracy with respect to the original representation. This study investigates two representation techniques, radial-basis function artificial neural networks and proper orthogonal decomposition, and implements each in an analytical target cascading problem formulation for electric vehicle powertrain system optimization. Implementation of each representation technique is demonstrated and the techniques are assessed in terms of efficiency (decision vector dimensionality) and accuracy. Keywords:Decomposition-based design optimization, analytical target cascading, coupling variables, reduced representation, vector-valued target. IntroductionIn formulating design optimization problems for large-scale, complex systems, it is often practical to separate these systems into simpler, more manageable subsystem configurations. Decomposition-based optimization strategies are used frequently to solve these problems. These strategies introduce consistency constraints [1], which contain coupling variables and ensure feasible design solutions. Coupling variables appear in subproblem optimization formulations as decision variables, increasing subproblem dimension. If these coupling variables consist of a small, finite number of scalars, problem size does not increase appreciably, and efficient optimization of the system is still possible. However, if coupling comprises infinite-dimensional variables, such as functions, ensuring consistency becomes computationally challenging. Discretization is typically applied, transforming infinite-dimensional variables into finite-dimensional ones, which can be represented in vector form aswhere y is the independent variable and q is the number of discretized points. Although this transformation enables consistency constraints to be computed, it requires a large number of discretization points to ensure a sufficiently accurate representation of the function. The dimensionality (measured by q) of these vector-valued coupling variables (VVCVs) can become very large, resulting in high-dimension subproblem decision vectors. Thus, it is desirable to approximate VVCVs with a reduced dimension representation that preserves sufficient accuracy. The new coupling variables comprising these representations are known as reduced representation variables [2]. With the exception of the work by Sobieski [3], published literature has not addressed this issue. The majority of published techniques use simplified models of objective functions and constraints to approximate their more comp...

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Target Cascading in Optimal System Design

Cited by 443 publications

References 12 publications

A classification of methods for distributed system optimization based on formulation structure

A classification of methods for distributed system optimization based on formulation structure

Overview of Methods for Multi-Level and/or Multi-Disciplinary Optimization

Reduced representations of vector-valued coupling variables in decomposition-based design optimization

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