Implementation of a trust region model management strategy in the DAKOTA optimization toolkit

2000 ◽  
Author(s):  
Anthony Giunta ◽  
Michael Eldred
Keyword(s):  
Management Strategy ◽  
Trust Region ◽  
Model Management ◽  
Region Model

scholarly journals Trust region model management in multidisciplinary design optimization

2000 ◽  
Vol 124 (1-2) ◽  
pp. 139-154 ◽  
Author(s):  
José F. Rodrı́guez ◽  
John E. Renaud ◽  
Brett A. Wujek ◽  
Ravindra V. Tappeta
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Trust Region ◽  
Model Management ◽  
Multidisciplinary Design ◽  
Region Model

Trust Region Augmented Lagrangian Methods for Sequential Response Surface Approximation and Optimization

1997 ◽  
Author(s):  
José F. Rodríguez ◽  
John E. Renaud ◽  
Layne T. Watson
Keyword(s):  
Response Surface ◽  
Computer Simulations ◽  
Design Problem ◽  
Augmented Lagrangian ◽  
Original Problem ◽  
Trust Region ◽  
Model Management ◽  
Response Surface Approximation ◽  
Approximate Optimization ◽  
Region Model

Abstract A common engineering practice is the use of approximation models in place of expensive computer simulations to drive a multidisciplinary design process based on nonlinear programming techniques. The use of approximation strategies is designed to reduce the number of detailed, costly computer simulations required during optimization while maintaining the pertinent features of the design problem. To date the primary focus of most approximate optimization strategies is that application of the method should lead to improved designs. This is a laudable attribute and certainly relevant for practicing designers. However to date few researchers have focused on the development of approximate optimization strategies that are assured of converging to a solution of the original problem. Recent works based on trust region model management strategies have shown promise in managing convergence in unconstrained approximate minimization. In this research we extend these well established notions from the literature on trust-region methods to manage the convergence of the more general approximate optimization problem where equality, inequality and variable bound constraints are present. The primary concern addressed in this study is how to manage the interaction between the optimization and the fidelity of the approximation models to ensure that the process converges to a solution of the original constrained design problem. Using a trust-region model management strategy, coupled with an augmented Lagrangian approach for constrained approximate optimization, one can show that the optimization process converges to a solution of the original problem. In this research an approximate optimization strategy is developed in which a cumulative response surface approximation of the augmented Lagrangian is sequentially optimized subject to a trust region constraint. Results for several test problems are presented in which convergence to a Karush-Kuhn-Tucker (KKT) point is observed.

A Reference Error Formulation for Multi-Fidelity Design Optimization

2017 ◽  
Author(s):  
Ahmed H. Bayoumy ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Computational Models ◽  
Design Space ◽  
Computational Cost ◽  
Trust Region ◽  
Model Management ◽  
Flexible Beam ◽  
Management Framework ◽  
Numerical Design ◽  
Region Model

We consider the problem of selecting among different computational models of various fidelity for evaluating the objective and constraint functions in numerical design optimization. Typically, higher-fidelity models are associated with higher computational cost. Therefore, it is desirable to employ them only when necessary. We introduce a reference error formulation that aims at determining whether lower-fidelity models (that are computationally cheaper) can be used in certain areas of the design space as the latter is being explored during the optimization process. The proposed approach is implemented using an existing trust region model management framework. We demonstrate the link between feasibility and fidelity and the key features of the proposed approach using the design example of a cantilever flexible beam subject to high accelerations.

Trust Region Augmented Lagrangian Methods for Sequential Response Surface Approximation and Optimization

1998 ◽  
Vol 120 (1) ◽  
pp. 58-66 ◽  
Author(s):  
J. F. Rodri´guez ◽  
J. E. Renaud ◽  
L. T. Watson
Keyword(s):  
Response Surface ◽  
Computer Simulations ◽  
Design Problem ◽  
Augmented Lagrangian ◽  
Original Problem ◽  
Trust Region ◽  
Model Management ◽  
Response Surface Approximation ◽  
Approximate Optimization ◽  
Region Model

A common engineering practice is the use of approximation models in place of expensive computer simulations to drive a multidisciplinary design process based on nonlinear programming techniques. The use of approximation strategies is designed to reduce the number of detailed, costly computer simulations required during optimization while maintaining the pertinent features of the design problem. To date the primary focus of most approximate optimization strategies is that application of the method should lead to improved designs. This is a laudable attribute and certainly relevant for practicing designers. However to date few researchers have focused on the development of approximate optimization strategies that are assured of converging to a solution of the original problem. Recent works based on trust region model management strategies have shown promise in managing convergence in unconstrained approximate minimization. In this research we extend these well established notions from the literature on trust-region methods to manage the convergence of the more general approximate optimization problem where equality, inequality and variable bound constraints are present. The primary concern addressed in this study is how to manage the interaction between the optimization and the fidelity of the approximation models to ensure that the process converges to a solution of the original constrained design problem. Using a trust-region model management strategy, coupled with an augmented Lagrangian approach for constrained approximate optimization, one can show that the optimization process converges to a solution of the original problem. In this research an approximate optimization strategy is developed in which a cumulative response surface approximation of the augmented Lagrangian is sequentially optimized subject to a trust region constraint. Results for several test problems are presented in which convergence to a Karush-Kuhn-Tucker (KKT) point is observed.

scholarly journals A Modified BFGS Formula Using a Trust Region Model for Nonsmooth Convex Minimizations

PLoS ONE ◽  
2015 ◽  
Vol 10 (10) ◽  
pp. e0140606
Author(s):  
Zengru Cui ◽  
Gonglin Yuan ◽  
Zhou Sheng ◽  
Wenjie Liu ◽  
Xiaoliang Wang ◽  
...  
Keyword(s):  
Trust Region ◽  
Region Model

Improved Trust Region Model Management for Approximate Optimization

1998 ◽  
Author(s):  
Brett A. Wujek ◽  
John E. Renaud
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Low Cost ◽  
Approximation Error ◽  
Trust Region ◽  
Model Management ◽  
Multidisciplinary Design ◽  
Test Problems ◽  
Approximation Techniques ◽  
Approximate Optimization

Abstract Approximations play an important role in multidisciplinary design optimization (MDO) by offering system behavior information at a relatively low cost. Most approximate optimization strategies are sequential in which an optimization of an approximate problem subject to design variable move limits is iteratively repeated until convergence. The move limits are imposed to restrict the optimization to regions of the design space in which the approximations provide meaningful information. In order to insure convergence of the sequence of approximate optimizations to a Karush Kuhn Tucker solution a move limit management strategy is required. In this paper, issues of move-limit management are reviewed and a new adaptive strategy for move limit management is developed. With its basis in the provably convergent trust region methodology, the TRAM (Trust region Ratio Approximation Method) strategy utilizes available gradient information and employs a backtracking process using various two-point approximation techniques to provide a flexible move-limit adjustment factor. The new strategy is successfully implemented in application to a suite of multidisciplinary design optimization test problems. These implementation studies highlight the ability of the TRAM strategy to control the amount of approximation error and efficiently manage the convergence to a Karush Kuhn Tucker solution.

scholarly journals Surrogate assisted interactive multiobjective optimization in energy system design of buildings

2021 ◽  
Author(s):  
Pouya Aghaei Pour ◽  
Tobias Rodemann ◽  
Jussi Hakanen ◽  
Kaisa Miettinen
Keyword(s):  
Decision Maker ◽  
Management Strategy ◽  
Computation Time ◽  
Energy System ◽  
Surrogate Models ◽  
Model Management ◽  
Interactive Methods ◽  
Objective Functions ◽  
Interactive Method ◽  
Computationally Expensive

AbstractIn this paper, we develop a novel evolutionary interactive method called interactive K-RVEA, which is suitable for computationally expensive problems. We use surrogate models to replace the original expensive objective functions to reduce the computation time. Typically, in interactive methods, a decision maker provides some preferences iteratively and the optimization algorithm narrows the search according to those preferences. However, working with surrogate models will introduce some inaccuracy to the preferences, and therefore, it would be desirable that the decision maker can work with the solutions that are evaluated with the original objective functions. Therefore, we propose a novel model management strategy to incorporate the decision maker’s preferences to select some of the solutions for both updating the surrogate models (to improve their accuracy) and to show them to the decision maker. Moreover, we solve a simulation-based computationally expensive optimization problem by finding an optimal configuration for an energy system of a heterogeneous business building complex. We demonstrate how a decision maker can interact with the method and how the most preferred solution is chosen. Finally, we compare our method with another interactive method, which does not have any model management strategy, and shows how our model management strategy can help the algorithm to follow the decision maker’s preferences.

scholarly journals A Relative Adequacy Framework for Multi-Model Management in Design Optimization

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Ahmed H. Bayoumy ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Computational Models ◽  
Computational Cost ◽  
Trust Region ◽  
Model Management ◽  
Flexible Beam ◽  
Flow Conditions ◽  
Management Framework ◽  
Derivative Free Optimization ◽  
Derivative Free

Abstract We consider the problem of selecting among different physics-based computational models of varying, and oftentimes not assessed, fidelity for evaluating the objective and constraint functions in numerical design optimization. Typically, higher-fidelity models are associated with higher computational cost. Therefore, it is desirable to employ them only when necessary. We introduce a relative adequacy framework that aims at determining whether lower-fidelity models (that are typically associated with lower computational cost) can be used in certain areas of the design space as the latter is being explored during the optimization process. We implement our approach by means of a trust-region management framework that utilizes the mesh adaptive direct search derivative-free optimization algorithm. We demonstrate the link between feasibility and fidelity and the key features of the proposed approach using two design optimization examples: a cantilever flexible beam subject to high accelerations and an airfoil in transonic flow conditions.

scholarly journals Simulation of selected management strategies on Canterbury sheep farms

1990 ◽  
pp. 211-215
Author(s):  
J.D. Finlayson ◽  
O.J. Cacho ◽  
A.C. Bywater
Keyword(s):  
Body Weight ◽  
Simulation Model ◽  
Management Strategy ◽  
Management Strategies ◽  
Model Management ◽  
Stocking Rate ◽  
Alternative Management ◽  
Sheep Farm ◽  
Gross Margins ◽  
Selection Of

A simulation model was used to investigate the effects of various combinations of stocking rate, drafting weight and lambing season on a hypothetical dryland farm in Canterbury. A selection of physical results is presented and financial impications of alternative management strategies are briefly discussed. Stocking rate and lambing time had considerable effects on animal performance, with minor effects from drafting weight. Gross margins were considerably affected by stocking rate; the highest return was obtained with conventional lambing at 15 su per ha and drafting lambs at 30 kg empty body weight. Keywords model, management strategy, sheep farm

PENGELOLAAN PEMBELAJARAN DIKLAT BERBASIS MODEL TEAMS LEARNING TOURNAMENT

2020 ◽  
Vol 12 (34) ◽  
Author(s):  
Firdos Mujahidin
Keyword(s):  
Learning Process ◽  
Management Strategy ◽  
Training Model ◽  
Learning Model ◽  
Model Management ◽  
Learning Teams ◽  
Individual Contributions ◽  
And Training

The background of this study is based on the still large amount of training learning that has not optimized the role of participants in the learning process while instilling and familiarizing attitudes. The purpose of this study is to describe the management strategy of training learning based on the Teams Learning Tournament (TLT) learning model. The method used in this study is the Pre-experimental method in the form of One-shot Case Study with the technique of collecting data through observing the results of giving action in learning. The results of the study show that the learning tournament-based training model management strategy is more effective in creating a fun learning process and fostering attitudes, especially discipline, responsibility and cooperation, so that the learning outcomes can be better. Because with the TLT model all training participants are required to show loyalty to their groups while producing quality group products, so that individual contributions are needed by the group. Groups formed can compete in a healthy manner.  Keywords: training, education and training learning, Teams Laerning Tournament learning model, managing strategies.

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