A constraint-based approach to feasibility assessment in preliminary 
design

Gurnani, Ashwin P.; Ferguson, Scott; Lewis, Kemper; Donndelinger, Joseph

doi:10.1017/s0890060406060252

Cited by 21 publications

(17 citation statements)

References 21 publications

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“…Gurnani et al . (Gurnani et al ., ) have developed meta‐modelling techniques along with Pareto frontier gap‐analyzer method for characterization of the Pareto frontier for assessing achievability of performance targets. Ferguson et al .…”

Section: Introductionmentioning

confidence: 84%

“…Previous research works in this area can be found in (Gurnani et al ., ) and (Ferguson et al ., ). Gurnani et al .…”

Section: Introductionmentioning

confidence: 99%

“…Previous research works in this area can be found in and . Gurnani et al (Gurnani et al, 2006) have developed meta-modelling techniques along with Pareto frontier gap-analyzer method for characterization of the Pareto frontier for assessing achievability of performance targets. have looked at the effect of convergence of a Multi-Objective Genetic Algorithm on obtaining the non-dominated points and have also looked at some techniques for mapping objective targets to potential design solutions.…”

Section: Introductionmentioning

confidence: 99%

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A Clustering‐based Methodology for Discontinuous Pareto Frontier Representation

Padmanabhan

2013

Multi Criteria Decision Anal

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This paper presents a methodology for capturing complex trade-off relationships among objective functions and the underlying design principles through Pareto frontier representations, for Multi-Objective Optimization problems in complex engineering design processes. The methodology provides engineers a way to quickly assess achievability of target values on objective functions (referred to as f-feasibility), and propose design solutions that meet or exceed these targets. Obtaining Pareto frontier representations can be challenging for cases when there are discontinuities in the Pareto frontiers and when the trade-off relationships vary across these discontinuities. The proposed methodology addresses this issue by first identifying discontinuities in the Pareto frontier through a clustering procedure, and then obtaining functional approximations of the trade-off relationships for each of the discontinuous portion of the Pareto frontier. A two-stage approach for clustering is employed. In the first stage, a DBSCAN clustering technique is used to classify disjoint Pareto sets in the objective space ( f-space) into different groups. In the second stage, a frequent set mining algorithm named ECLAT and a subsequent filtering procedure are used to extract design constraint combinations that are active (binding) within each of these groups to form subgroups. Approximations are obtained for each of the subgroups using a constrained least squares technique, which are then combined to obtain an overall equation for assessing f-feasibility of any arbitrary point in objective space. In the two implementation studies, it is observed that the two-stage approach for clustering is highly effective in yielding good quality Pareto frontier representations and f-feasibility assessments.

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Section: Introductionmentioning

confidence: 84%

“…Previous research works in this area can be found in (Gurnani et al ., ) and (Ferguson et al ., ). Gurnani et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Clustering‐based Methodology for Discontinuous Pareto Frontier Representation

Padmanabhan

2013

Multi Criteria Decision Anal

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“…If certain conditions are not met, then no further function evaluations take place. This type of strategy is used in conjunction with an optimization framework by Paredis [36], and by Gurnani et al [16,17].…”

Section: Approximation and Surrogate-based Approachesmentioning

confidence: 99%

Variable Fidelity Modeling as Applied to Trajectory Optimization for a Hydraulic Backhoe

Moore

Paredis

2009

Volume 5: 35th Design Automation Conference, Parts a and B

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Modeling, simulation, and optimization play vital roles throughout the engineering design process; however, in many design disciplines the cost of simulation is high, and designers are faced with a tradeoff between the number of alternatives that can be evaluated and the accuracy with which they are evaluated. In this paper, a methodology is presented for using models of various levels of fidelity during the optimization process. The intent is to use inexpensive, low-fidelity models with limited accuracy to recognize poor design alternatives and reserve the high-fidelity, accurate, but also expensive models only to characterize the best alternatives. Specifically, by setting a user-defined performance threshold, the optimizer can explore the design space using a low-fidelity model by default, and switch to a higher fidelity model only if the performance threshold is attained. In this manner, the high fidelity model is used only to discern the best solution from the set of good solutions, so computational resources are conserved until the optimizer is close to the solution. This makes the optimization process more efficient without sacrificing the quality of the solution. The method is illustrated by optimizing the trajectory of a hydraulic backhoe. To characterize the robustness and efficiency of the method, a design space exploration is performed using both the low and high fidelity models, and the optimization problem is solved multiple times using the variable fidelity framework.

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“…Whether exploring a multiobjective performance space or a multidimensional tradespace, a designer must make a final selection from an often large number of alternatives. In multiobjective optimization problems, a designer must navigate the tradeoffs between competing objectives [1][2][3] and select the location on the Pareto frontier that best satisfies the preference structure of the designer. Similarly, tradespace analysis involves executing broad trade studies to understand which designs meet minimum requirements while locating those designs that best meet the goal of the project [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives

Foster¹,

Ferguson

2013

Journal of Computing and Information Science in Engineering

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The objective of this paper is to demonstrate that unique alternative designs can be efficiently found by searching the discarded data (or graveyard) from a multiobjective genetic algorithm (MOGA). Motivation for using graveyard data to generate design alternatives arises from the computational cost associated with real-time design space exploration of multiobjective optimization problems. The effectiveness of this approach is explored by comparing (1) the uniqueness of alternatives found using graveyard data and those generated using an optimization-based search, and (2) how alternative generation near the Pareto frontier is impacted. Two multiobjective case study problems are introduced-a two bar truss and an I-beam design optimization. Results from these studies indicate that using graveyard data allows for the discovery of alternative designs that are at least 70% as unique as alternatives found using an optimization-based alternative identification approach, while saving a significant number of functional evaluations. Additionally, graveyard data are shown to be better suited for alternative generation near the Pareto frontier than standard sampling techniques. Finally, areas of future work are also discussed.

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A constraint-based approach to feasibility assessment in preliminary design

Cited by 21 publications

References 21 publications

A Clustering‐based Methodology for Discontinuous Pareto Frontier Representation

A Clustering‐based Methodology for Discontinuous Pareto Frontier Representation

Variable Fidelity Modeling as Applied to Trajectory Optimization for a Hydraulic Backhoe

Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives

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