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31
Comparative Properties Of Collaborative Optimization And Other Approaches To Mdo
, 1999
"... We discuss criteria by which one can classify, analyze, and evaluate approaches to solving multidisciplinary design optimization (MDO) problems. Central to our discussion is the often overlooked distinction between questions of formulating MDO problems and solving the resulting computational problem ..."
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Cited by 24 (2 self)
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We discuss criteria by which one can classify, analyze, and evaluate approaches to solving multidisciplinary design optimization (MDO) problems. Central to our discussion is the often overlooked distinction between questions of formulating MDO problems and solving the resulting computational problem. We illustrate our general remarks by comparing several approaches to MDO that have been proposed. INTRODUCTION There are likely as many definitions of multidisciplinary design optimization (MDO) as there are areas and phases of design. For our discussion, we shall take MDO to mean the systematic approach to optimization of complex, coupled engineering systems, where "multidisciplinary " refers to the different aspects that must be included in a design problem. For instance, the design of aircraft involves, among other disciplines, aerodynamics, structural analysis, propulsion, and control. See SobieszczanskiSobieski and Haftka (1997), Alexandrov and Hussaini (1997) for overviews of the ...
An Efficient Weighting Update Method to Achieve Acceptable Consistency Deviation in Analytical Target Cascading
 ASME J. Mech. Des
, 2005
"... Information Information in Engineering in Engineering Conference Conference ..."
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Cited by 18 (13 self)
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Information Information in Engineering in Engineering Conference Conference
Initial Results Of An Mdo Method Evaluation Study
, 1998
"... The NASA Langley MDO method evaluation study seeks to arrive at a set of guidelines for using promising MDO methods by accumulating and analyzing computational data for such methods. The data are collected by conducting a series of reproducible experiments. In the first phase of the study, three MDO ..."
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Cited by 16 (6 self)
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The NASA Langley MDO method evaluation study seeks to arrive at a set of guidelines for using promising MDO methods by accumulating and analyzing computational data for such methods. The data are collected by conducting a series of reproducible experiments. In the first phase of the study, three MDO methods were implemented in the iSIGHT z framework and used to solve a set of ten relatively simple problems. In this paper, we comment on the general considerations for conducting method evaluation studies and report some initial results obtained to date. In particular, although the results are not conclusive because of the small initial test set, preliminary numbers suggest that the performance of the methods tends to be consistent with their predicted theoretical properties. Key Words: Multidisciplinary Design Optimization, Method Evaluation AMS Subject Classification: 65K05, 49M37 Introduction Multidisciplinary Design Optimization (MDO) problems are optimization problems that desc...
Optimal partitioning and coordination decisions in decompositionbased design optimization
, 2008
"... The solution of complex system design problems using decompositionbased optimization methods requires determination of appropriate problem partitioning and coordination strategies. Previous optimal partitioning techniques have not addressed the coordination issue explicitly. This article presents a ..."
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Cited by 13 (6 self)
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The solution of complex system design problems using decompositionbased optimization methods requires determination of appropriate problem partitioning and coordination strategies. Previous optimal partitioning techniques have not addressed the coordination issue explicitly. This article presents a formal approach to simultaneous partitioning and coordination strategy decisions that can provide insights on whether a decompositionbased method will be effective for a given problem. Paretooptimal solutions are generated to quantify tradeoffs between the sizes of subproblems and coordination problems as measures of the computational costs resulting from different partitioning and coordination strategies. Promising preliminary results with small test problems are presented. The approach is illustrated on an electric water pump design problem. �DOI: 10.1115/1.3178729� 1
An augmented Lagrangian decomposition method for quasiseparable problems in MDO
"... Several decomposition methods have been proposed for the distributed optimal design of quasiseparable problems encountered in Multidisciplinary Design Optimization (MDO). Some of these methods are known to have numerical convergence difficulties that can be explained theoretically. We propose a new ..."
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Cited by 5 (0 self)
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Several decomposition methods have been proposed for the distributed optimal design of quasiseparable problems encountered in Multidisciplinary Design Optimization (MDO). Some of these methods are known to have numerical convergence difficulties that can be explained theoretically. We propose a new decomposition algorithm for quasiseparable MDO problems. In particular, we propose a decomposed problem formulation based on the augmented Lagrangian penalty function and the block coordinate descent algorithm. The proposed solution algorithm consists of inner and outer loops. In the outer loop, the augmented Lagrangian penalty parameters are updated. In the inner loop, our method alternates between solving an optimization master problem, and solving disciplinary optimization subproblems. The coordinating master problem can be solved analytically; the disciplinary subproblems can be solved using commonly available gradientbased optimization algorithms. The augmented Lagrangian decomposition method is derived such that existing proofs can be used to show convergence of the decomposition algorithm to KKT points of the original problem under mild assumptions. We investigate the numerical performance of the proposed method on two example problems. I.
Globallocal Structural Optimization Using Response Surfaces of Local Optimization Margins
"... A general decomposition method developed by the authors is applied to globallocal structural optimization problems. First, a large number of component optimizations for maximization of margins are performed. Response surface approximations (RSA) for maximum margins of component optimization are con ..."
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Cited by 5 (1 self)
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A general decomposition method developed by the authors is applied to globallocal structural optimization problems. First, a large number of component optimizations for maximization of margins are performed. Response surface approximations (RSA) for maximum margins of component optimization are constructed. At the system level optimization, the RSA of maximum margins are used as surrogate for the components. One advantage of the decomposition approach is that it allows much of the search for a global optimum to be conducted in lowdimensions for each component separately. Minimization of a portal frame weight with eight local optima is used to demonstrate the approach.
A sequential Linear Programming Coordination Algorithm for Analytical Target Cascading
 ASME Paper
, 2007
"... Decompositionbased strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy i ..."
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Cited by 5 (5 self)
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Decompositionbased strategies, such as analytical target cascading (ATC), are often employed in design optimization of complex systems. Achieving convergence and computational efficiency in the coordination strategy that solves the partitioned problem is a key challenge. A new convergent strategy is proposed for ATC, which coordinates the interactions among subproblems using sequential lineralizations. Linearity of subproblems is maintained using L ∞ norms to measure deviations between targets and responses. A subproblem suspension strategy is used to temporarily suspend inclusion of subproblems that do not need significant redesign, based on trust region and target value step size. The proposed strategy is intended for use in optimization problems where sequential linearizations are typically effective, such as problems with extensive monotonicities, large number of constraints relative to variables, and propagation of probabilities with normal distributions. Experiments with test problems show that, relative to standard ATC coordination, the number of subproblem evaluations is reduced considerably while maintaining accuracy. 1
A Coevolutionary Architecture for Distributed Optimization of Complex Coupled Systems
 AIAA JOURNAL
, 2002
"... This paper presents a coevolutionary architecture for distributed optimization of complex coupled systems. This architecture is inspired by the phenomena of coevolutionary adaptation occurring in ecological systems. The focus ..."
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Cited by 4 (1 self)
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This paper presents a coevolutionary architecture for distributed optimization of complex coupled systems. This architecture is inspired by the phenomena of coevolutionary adaptation occurring in ecological systems. The focus
Collaborative Reliability Analysis for Multidisciplinary Systems Design
, 2002
"... Traditional Multidisciplinary Design Optimization (MDO) generates deterministic optimal designs, which are frequently pushed to the limits of design constraint boundaries, leaving little or no room to accommodate uncertainties in system input, modeling, and simulation. As a result, the design soluti ..."
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Cited by 4 (0 self)
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Traditional Multidisciplinary Design Optimization (MDO) generates deterministic optimal designs, which are frequently pushed to the limits of design constraint boundaries, leaving little or no room to accommodate uncertainties in system input, modeling, and simulation. As a result, the design solution obtained may be highly sensitive to the variations of system input which will lead to performance loss and the solution is often risky (high likelihood of undesired events). Reliabilitybased design is one of the alternative techniques for design under uncertainty. The natural method to perform reliability analysis in multidisciplinary systems is the allinone approach where the existing reliability analysis techniques are applied directly to the systemlevel multidisciplinary analysis. However, the allonone reliability analysis method requires a double loop procedure and therefore is generally very time consuming. To improve the efficiency of reliability analysis under the MDO framework, a collaborative reliability analysis method is proposed in this paper. The procedure of the traditional Most Probable Point (MPP) based reliability analysis method is combined with the collaborative disciplinary analyses to automatically satisfy the interdisciplinary consistency in reliability analysis. As a result, only a single loop procedure is required and all the computations are conducted concurrently at the individual disciplinelevel. Compared with the existing reliability analysis methods in MDO, the proposed method is more efficient and therefore provides a cheaper tool to evaluate design feasibility in MDO under uncertainty. Two examples are used for the purpose of verification.
J.R.R.A.: Comparison of mdo architectures within a universal framework
 In: 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Newport, Rhode Island (2006). AIAA
"... A new MDO framework has been developed in Python to provide an ideal platform for comparisons relating the performance of various MDO architectures. Specifically, it eliminates the need for reformulation while solving MDO problems in multiple disciplines. Once a problem has been implemented, its sol ..."
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A new MDO framework has been developed in Python to provide an ideal platform for comparisons relating the performance of various MDO architectures. Specifically, it eliminates the need for reformulation while solving MDO problems in multiple disciplines. Once a problem has been implemented, its solution can be obtained from any architecture present in the framework. In addition to providing a performance comparison of the more common architectures, the modular design of the framework allows rapid testing of newly developed architectures. Results generated from this study provide a strong foundation for identifying the performance trends of various architectures with a variety of problem classes.