Visualizing the optimization process in real-time using physical programming

Messac, Achille; Chen, Xuan

doi:10.2514/6.1998-4708

Cited by 13 publications

(17 citation statements)

References 12 publications

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“…This method identifies how the responses of the physical model will change with changes in the design variables as optimization is running. Messac and Chen (2000) suggested a method for real-time visualization of the optimization process. The technique for the visualization of the path through the design space of the solutions during evolutionary optimization run was developed by Pohlheim (1999).…”

Section: Visual Design Steeringmentioning

confidence: 99%

Multi-Objective Design Exploration and its Applications

Obayashi¹,

Jeong²,

Shimoyama³

et al. 2010

International Journal of Aeronautical and Space Sciences

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Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.

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Section: Visual Design Steeringmentioning

confidence: 99%

Multi-Objective Design Exploration and its Applications

Obayashi¹,

Jeong²,

Shimoyama³

et al. 2010

International Journal of Aeronautical and Space Sciences

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“…Readers interested in more detailed discussion of the advantages and disadvantages of ABV and non-ABV are directed to [11] for more information.…”

Section: American Institute Of Aeronautics and Astronauticsmentioning

confidence: 99%

“…The following sections describe some of the features of CVis. Real-time visualization methods can be classified into two general categories: Artifact-Based Visualization (ABV) schemes and Non-ArtifactBased Visualization (Non-ABV) schemes [11]. Artifact-based schemes are those that are typically imposed on a physical object with a prescribed geometry, such as representing the stress in a beam by color contours imposed on the beams geometry.…”

Section: Cloud Visualization Descriptionmentioning

confidence: 99%

“…Until these innovations, computers were solely relied on to interpret results and compute answers based on programs written. It is now possible to interact with these large datasets even while they are being used in running analyses [5][6][7][8][9][10][11]. Users have the ability to compress large amounts of data into a visual format, to investigate trends and relationships that could not be seen otherwise, and then make informed decisions regarding a product or process design.…”

Section: Introductionmentioning

confidence: 99%

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Multidimensional Design Visualization In Multiobjective Optimization

Eddy¹,

Lewis²

2002

9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization

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Some of the first attempts at using visualization methods to aid decisions in design and optimization are found in [1]. More recent advances in computer visualization and Virtual Reality (VR) [2][3][4] are allowing designers and scientists to interact and manipulate vast amounts of data. Until these innovations, computers were solely relied on to interpret results and compute answers based on programs written. It is now possible to interact with these large datasets even while they are being used in running analyses [5][6][7][8][9][10][11]. Users have the ability to compress large amounts of data into a visual format, to investigate trends and relationships that could not be seen otherwise, and then make informed decisions regarding a product or process design.As our ability to generate more and more data for increasingly large engineering models improves, the need for means of managing that data becomes greater. Information management from a decisionmaking perspective involves being able to capture and then represent significant information to a designer so that he or she can make effective and efficient decisions. However, most visualization techniques used in engineering, such as graphs and charts, are limited to two-dimensional representations and at most three-dimensional representations. In this paper, we present a technique used to capture and represent engineering information in a multidimensional context. In this paper, we present an overview of the technique and provide details regarding two specialized functions for use in multidimensional and multiobjective optimization. This method is part of an effort to develop a comprehensive visualization-based optimization tool for multi-objective and robust design problems.

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“…Previous interactive methods are described in (Tappeta and Renaud, 1999;Tappeta et al, 2000;Azarm and Narayanan, 2000), but none of these methods makes use of a decomposition as suggested by the approach presented in this paper. Motivated by the several recent studies emphasizing the importance of visualizing the optimization process (Messac and Chen, 2000), the design data (Eddy and Lewis, 2002) and Pareto frontiers (Agrawal et al, 2004(Agrawal et al, , 2005Mattson and Messac, 2005), the new methodology includes the feature of visualizing the tradeoff curves for every subproblem to support the choice of an optimal design. In addition to the tradeoff between those objectives participating in the same subproblem, information on the tradeoffs between different subproblems is obtained from a sensitivity analysis and used for the subsequent coordination.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

2D decision-making for multicriteria design optimization

Engau

Wiecek

2006

Struct Multidisc Optim

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. AbstractThe high dimensionality encountered in engineering design optimization due to large numbers of performance criteria and specifications leads to cumbersome and sometimes unachievable tradeoff analyses. To facilitate those analyses and enhance decision-making and design selection, we propose to decompose the original problem by considering only pairs of criteria at a time, thereby making tradeoff evaluation the simplest possible. For the final design integration, we develop a novel coordination mechanism that guarantees that the selected design is also preferred for the original problem. The solution of an overall large-scale problem is therefore reduced to solving a family of bi-criteria subproblems and allows designers to effectively use decision-making in merely two dimensions for multi-criteria design optimization.

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Visualizing the optimization process in real-time using physical programming

Cited by 13 publications

References 12 publications

Multi-Objective Design Exploration and its Applications

Multi-Objective Design Exploration and its Applications

Multidimensional Design Visualization In Multiobjective Optimization

2D decision-making for multicriteria design optimization

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