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InformationTheoretic Control of Multiple Sensor Platforms
, 2002
"... Ben Grocholsky Doctor of Philosophy The University of Sydney March 2002 InformationTheoretic Control of This thesis is concerned with the development of a consistent, informationtheoretic basis for understanding of coordination and cooperation decentralised multisensor multiplatform systems. Au ..."
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Cited by 46 (4 self)
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Ben Grocholsky Doctor of Philosophy The University of Sydney March 2002 InformationTheoretic Control of This thesis is concerned with the development of a consistent, informationtheoretic basis for understanding of coordination and cooperation decentralised multisensor multiplatform systems. Autonomous systems composed of multiple sensors and multiple platforms potentially have significant importance in applications such as defence, search and rescue, mining or intelligent manufacturing. However, the e#ective use of multiple autonomous systems requires that an understanding be developed of the mechanisms of coordination and cooperation between component systems in pursuit of a common goal. A fundamental, quantitative, understanding of coordination and cooperation between decentralised autonomous systems is the main goal of this thesis.
Development and Application of the Collaborative Optimization Architecture in a Multidisciplinary Design Environment
 Multidisciplinary Design Optimization: State of the Art
, 1996
"... Collaborative optimization is a design architecture applicable in any multidisciplinary analysis environment but specifically intended for largescale distributed analysis applications. In this approach, a complex problem is hierarchically decomposed along disciplinary boundaries into a number of su ..."
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Cited by 30 (3 self)
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Collaborative optimization is a design architecture applicable in any multidisciplinary analysis environment but specifically intended for largescale distributed analysis applications. In this approach, a complex problem is hierarchically decomposed along disciplinary boundaries into a number of subproblems which are brought into multidisciplinary agreement by a systemlevel coordination process. When applied to problems in a multidisciplinary design environment, this scheme has several advantages over traditional solution strategies. These advantageous features include reducing the amount of information transferred between disciplines, the removal of large iterationloops, allowing the use of different subspace optimizers among the various analysis groups, an analysis framework which is easily parallelized and can operate on heterogenous equipment, and a structural framework that is wellsuited for conventional disciplinary organizations. In this article, the collaborative architectu...
A Hypergraph Framework For Optimal ModelBased Decomposition Of Design Problems
 Computational Optimization and Applications
, 1997
"... Decomposition of large engineering system models is desirable since increased model size reduces reliability and speed of numerical solution algorithms. The article presents a methodology for optimal modelbased decomposition (OMBD) of design problems, whether or not initially cast as optimization p ..."
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Cited by 30 (20 self)
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Decomposition of large engineering system models is desirable since increased model size reduces reliability and speed of numerical solution algorithms. The article presents a methodology for optimal modelbased decomposition (OMBD) of design problems, whether or not initially cast as optimization problems. The overall model is represented by a hypergraph and is optimally partitioned into weakly connected subgraphs that satisfy decomposition constraints. Spectral graphpartitioning methods together with iterative improvement techniques are proposed for hypergraph partitioning. A known spectral Kpartitioning formulation, which accounts for partition sizes and edge weights, is extended to graphs with also vertex weights. The OMBD formulation is robust enough to account for computational demands and resources and strength of interdependencies between the computational modules contained in the model. KEYWORDS: Model decomposition, multidisciplinary design, hypergraph partitioning, larges...
Sequential Approximate Optimization Using Variable Fidelity Response Surface Approximations
 Structural and Multidisciplinary Optimization
, 2000
"... The dimensionality and complexity of typical multidisciplinary systems hinders the use of formal optimization techniques in application to this class of problems. The use of approximations to represent the system design metrics and constraints has become vital for achieving good performance in many ..."
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Cited by 19 (8 self)
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The dimensionality and complexity of typical multidisciplinary systems hinders the use of formal optimization techniques in application to this class of problems. The use of approximations to represent the system design metrics and constraints has become vital for achieving good performance in many multidisciplinary design optimization (MDO) algorithms. This paper reports resent research efforts on the use of variable fidelity response surface approximations (RSA) to drive the convergence of MDO problems using a trust region model management algorithm. The present study focuses on a comparative study of different response sampling strategies based on design of experiment (DOE) approaches within the disciplines to generate the zero order data to build the RSAs. Two MDO test problems that have complex coupling between disciplines are used to benchmark the performance of each sampling strategy. The results show that these types of variable fidelity RSAs can be eectively managed by the trust region model management strategy to drive convergence of MDO problems. It is observed that the efficiency of the optimization algorithm depends on the sampling strategy used. A comparison of the DOE approaches with those obtained using a optimization based sampling strategy (i.e., concurrent subspace optimization  CSSO) show the DOE methodologies to be competitive with the CSSO based sampling methodology in some cases. However, the CSSO based sampling strategy was found to be...
Constructing Variable Fidelity Response Surface Approximations In The Usable Feasible Region
, 2000
"... The use of Response Surface Approximation (RSA) within an approximate optimization framework for the design of complex systems has increased as designers are challenged to develop better designs in reduced times. Traditionally, statistical sampling techniques (i. e., experimental design) have been u ..."
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Cited by 18 (9 self)
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The use of Response Surface Approximation (RSA) within an approximate optimization framework for the design of complex systems has increased as designers are challenged to develop better designs in reduced times. Traditionally, statistical sampling techniques (i. e., experimental design) have been used for constructing RSA's. These statistical sampling techniques are designed to be space filling, so that the response surface approximations are predictive across the range of the design sample space. When used in sequential approximate optimization strategies, a portion of the samples can be in the infeasible and/or ascent regions of the design space. These samples can bias the resulting RSA and make it less predictive in the usable feasible region where the optimization takes place. In the response surface based concurrent subsace optimization approach the design sampling strategy for RSA construction is optimization based. This optimization based sampling has proved to be effective due to the fact it samples in the linearized usable feasible region. In the present research, an experimental design strategy for projecting data points in the linearized usable feasible region is developed for constructing RSA's. The technique is implemented in a Sequential Approximate Optimization framework and tested in application to two multidisciplinary design optimization (MDO) test problems. Results show that the proposed technique pro
AIRCRAFT MULTIDISCIPLINARY DESIGN OPTIMIZATION USING DESIGN OF EXPERIMENTS THEORY AND RESPONSE SURFACE MODELING METHODS
, 1997
"... Design engineers often employ numerical optimization techniques to assist in the evaluation and comparison of new aircraft configurations. While the use of numerical optimization methods is largely successful, the presence of numerical noise in realistic engineering optimization problems often inhib ..."
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Cited by 18 (2 self)
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Design engineers often employ numerical optimization techniques to assist in the evaluation and comparison of new aircraft configurations. While the use of numerical optimization methods is largely successful, the presence of numerical noise in realistic engineering optimization problems often inhibits the use of many gradientbased optimization techniques. Numerical noise causes inaccurate gradient calculations which in turn slows or prevents convergence during optimization. The problems created by numerical noise are particularly acute in aircraft design applications where a single aerodynamic or structural analysis of a realistic aircraft configuration may require tens of CPU hours on a supercomputer. The computational expense of the analyses coupled with the convergence difficulties created by numerical noise are significant obstacles to performing aircraft multidisciplinary design optimization. To address these issues, a procedure has been developed to create two types of noisefree mathematical models for use in aircraft optimization studies. These two methods use elements of statistical analysis and the overall procedure for using the methods is made computationally affordable by the application of parallel computing techniques. The first
Adaptive Experimental Design For Construction Of Response Surface Approximations
, 2001
"... Sequential Approximate Optimization (SAO) is a class of methods available for the multidisciplinary design optimization (MDO) of complex systems that are composed of several disciplines coupled together. One of the approaches used for SAO, is based on a quadratic response surface approximation, wher ..."
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Cited by 15 (9 self)
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Sequential Approximate Optimization (SAO) is a class of methods available for the multidisciplinary design optimization (MDO) of complex systems that are composed of several disciplines coupled together. One of the approaches used for SAO, is based on a quadratic response surface approximation, where zero and first order information are required. In these methods, designers must generate and query a database of order O(n²) in order to compute the second order terms of the quadratic response surface approximation. As the number of design variables grows, the computational cost of generating the required database becomes a concern. In this paper, we present an new approach in which we require just O(n) parameters for constructing a second order approximation. This is accomplished by transforming the matrix of second order terms into the canonical form. The method periodically requires an order O(n²) update of the second order approximation to maintain accuracy. Results show
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 14 (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...
Response Surface Based, Concurrent Subspace Optimization For Multidisciplinary System Design
 34th AIAA Aerospace Sciences Meeting and Exhibit
, 1996
"... The analysis of engineering systems must often be conducted using complex, nonhierarchic, coupled, disciplinespecific methods. When the cost of performing these individual analyses is high, it is impractical to apply many current optimization methods to this type of system to achieve improved desi ..."
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Cited by 14 (10 self)
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The analysis of engineering systems must often be conducted using complex, nonhierarchic, coupled, disciplinespecific methods. When the cost of performing these individual analyses is high, it is impractical to apply many current optimization methods to this type of system to achieve improved designs. Consequently, methods are being developed which attempt to reduce the cost of designing or optimizing nonhierarchic systems. This paper details the application of an extension of the Concurrent Subspace Optimization (CSSO) approach through the use of neural network based response surface mappings. The response surface mappings are used to allow the discipline designer to account for discipline coupling and the impact of design decisions on the system at the discipline level as well as for system level design coordination. The ability of this method to identify globally optimal designs is discussed using two example system design problems. Comparisons between this algorithm and full sys...
Hierarchical Overlapping Coordination for LargeScale Optimization by Decomposition
, 1999
"... Decomposition of large engineering design problems into smaller design subproblems enhances robustness and speed of numerical solution algorithms. Design subproblems can be solved in parallel, using the optimization technique most suitable for the underlying subproblem. This also reflects the typica ..."
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Cited by 14 (9 self)
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Decomposition of large engineering design problems into smaller design subproblems enhances robustness and speed of numerical solution algorithms. Design subproblems can be solved in parallel, using the optimization technique most suitable for the underlying subproblem. This also reflects the typical multidisciplinary nature of system design problems and allows better interpretation of results. Hierarchical overlapping coordination (HOC) simultaneously uses two or more problem decompositions, each of them associated with di#erent partitions of the design variables and constraints. Coordination is achieved by the exchange of information between decompositions. This article presents the HOC algorithm and a su#cient condition for global convergence of the algorithm to the solution of a convex optimization problem. The convergence condition involves the rank of a matrix derived from the Jacobian of the constraints. Computational results obtained by applying the HOC algorithm to problems o...