Visual Steering Commands and Test Problems to Support Research in Trade Space Exploration

2008 ◽  
Author(s):  
Timothy Simpson ◽  
David Spencer ◽  
Michael Yukish ◽  
Gary Stump
Keyword(s):  
Space Exploration ◽  
Test Problems ◽  
Trade Space Exploration ◽  
Support Research

scholarly journals Improving the Performance of Visual Steering Commands for Multi-Dimensional Trade Space Exploration

2008 ◽  
Author(s):  
Christopher D. Congdon ◽  
Daniel E. Carlsen ◽  
Timothy W. Simpson ◽  
Jay D. Martin
Keyword(s):  
Optimization Problems ◽  
Space Exploration ◽  
Engineering Application ◽  
Region Of Interest ◽  
Monte Carlo Sampling ◽  
Test Problems ◽  
Design Decision ◽  
Support Design ◽  
Exploration Process ◽  
Trade Space Exploration

Designers perform many tasks when developing new products and systems, and making decisions may be among the most important of these tasks. The trade space exploration process advocated in this work provides a visual and intuitive approach for formulating and solving single- and multi-objective optimization problems to support design decision-making. In this paper, we introduce an advanced sampling method to improve the performance of the visual steering commands that have been developed to explore and navigate the trade space. This method combines speciation and crowding operations used within the Differential Evolution (DE) algorithm to generate new samples near the region of interest. The accuracy and diversity of the resulting samples are compared against simple Monte Carlo sampling as well as the current implementation of the visual steering commands using a suite of test problems and an engineering application. The proposed method substantially increases the efficiency and effectiveness of the sampling process while maintaining diversity within the trade space.

The Importance of Training for Interactive Trade Space Exploration: A Study of Novice and Expert Users

2011 ◽  
Vol 11 (3) ◽  
Author(s):  
David Wolf ◽  
Jennifer Hyland ◽  
Timothy W. Simpson ◽  
Xiaolong (Luke) Zhang
Keyword(s):  
Space Exploration ◽  
Design Decision ◽  
Support Design ◽  
Computing Power ◽  
Training Video ◽  
Engine Combustion ◽  
Interactive Data ◽  
Effectiveness And Efficiency ◽  
Trade Space Exploration ◽  
Future Work

Thanks to recent advances in computing power and speed, engineers can now generate a wealth of data on demand to support design decision-making. These advances have enabled new approaches to search multidimensional trade spaces through interactive data visualization and exploration. In this paper, we investigate the effectiveness and efficiency of interactive trade space exploration strategies by conducting human subject experiments with novice and expert users. A single objective, constrained design optimization problem involving the sizing of an engine combustion chamber is used for this study. Effectiveness is measured by comparing the best feasible design obtained by each user, and efficiency is assessed based on the percentage of feasible designs generated by each user. Results indicate that novices who watch a 5-min training video before the experiment obtain results that are not significantly different from those obtained by expert users, and both groups are statistically better than the novices without the training video in terms of effectiveness and efficiency. Frequency and ordering of the visualization and exploration tools are also compared to understand the differences in each group’s search strategy. The implications of the results are discussed along with future work.

A Preliminary Study of Novice and Expert Users’ Decision-Making Procedures During Visual Trade Space Exploration

2009 ◽  
Author(s):  
David Wolf ◽  
Timothy W. Simpson ◽  
Xiaolong Luke Zhang
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Data Visualization ◽  
Space Exploration ◽  
Computational Thinking ◽  
Test Group ◽  
Design Decision ◽  
Ongoing Research ◽  
Trade Space Exploration ◽  
Insight Into

Thanks to recent advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support multi-dimensional data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expert-driven exploration that is being enabled by computational thinking. In this paper, we discuss ongoing research that seeks to transform decades-old decision-making paradigms rooted in operations research by considering how to effectively convert data into knowledge that enhances decision-making and leads to better designs. Specifically, we address decision-making within the area of trade space exploration by conducting human-computer interaction studies using multi-dimensional data visualization software that we have been developing. We first discuss a Pilot Study that was conducted to gain insight into expected differences between novice and expert decision-makers using a small test group. We then present the results of two Preliminary Experiments designed to gain insight into procedural differences in how novices and experts use multi-dimensional data visualization and exploration tools and to measure their ability to use these tools effectively when solving an engineering design problem. This work supports our goal of developing training protocols that support efficient and effective trade space exploration.

Quantifying the Shape of Pareto Fronts During Multi-Objective Trade Space Exploration

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Mehmet Unal ◽  
Gordon P. Warn ◽  
Timothy W. Simpson
Keyword(s):  
Pareto Front ◽  
Space Exploration ◽  
Problem Formulation ◽  
Shape Index ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Complex Engineering ◽  
Trade Space Exploration ◽  
Pareto Fronts ◽  
Future Work

Recent advances in simulation and computation capabilities have enabled designers to model increasingly complex engineering problems, taking into account many dimensions, or objectives, in the problem formulation. Increasing the dimensionality often results in a large trade space, where decision-makers (DM) must identify and negotiate conflicting objectives to select the best designs. Trade space exploration often involves the projection of nondominated solutions, that is, the Pareto front, onto two-objective trade spaces to help identify and negotiate tradeoffs between conflicting objectives. However, as the number of objectives increases, an exhaustive exploration of all of the two-dimensional (2D) Pareto fronts can be inefficient due to a combinatorial increase in objective pairs. Recently, an index was introduced to quantify the shape of a Pareto front without having to visualize the solution set. In this paper, a formal derivation of the Pareto Shape Index is presented and used to support multi-objective trade space exploration. Two approaches for trade space exploration are presented and their advantages are discussed, specifically: (1) using the Pareto shape index for weighting objectives and (2) using the Pareto shape index to rank objective pairs for visualization. By applying the two approaches to two multi-objective problems, the efficiency of using the Pareto shape index for weighting objectives to identify solutions is demonstrated. We also show that using the index to rank objective pairs provides DM with the flexibility to form preferences throughout the process without closely investigating all objective pairs. The limitations and future work are also discussed.

P3GA: An Algorithm for Technology Characterization

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Edgar Galvan ◽  
Richard J. Malak
Keyword(s):  
Design Automation ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Numerical Test ◽  
Pareto Frontier ◽  
Test Problems ◽  
Genetic Optimization ◽  
Technology Evaluation ◽  
Mathematical Basis

It is important for engineers to understand the capabilities and limitations of the technologies they consider for use in their systems. However, communicating this information can be a challenge. Mathematical characterizations of technical capabilities are of interest as a means to reduce ambiguity in communication and to increase opportunities to utilize design automation methods. The parameterized Pareto frontier (PPF) was introduced in prior work as a mathematical basis for modeling technical capabilities. One advantage of PPFs is that, in many cases, engineers can model a system by composing frontiers of its components. This allows for rapid technology evaluation and design space exploration. However, finding the PPF can be difficult. The contribution of this article is a new algorithm for approximating the PPF, called predictive parameterized Pareto genetic algorithm (P3GA). The proposed algorithm uses concepts and methods from multi-objective genetic optimization and machine learning to generate a discrete approximation of the PPF. If needed, designers can generate a continuous approximation of the frontier by generalizing beyond these data. The algorithm is explained, its performance is analyzed on numerical test problems, and its use is demonstrated on an engineering example. The results of the investigation indicate that P3GA may be effective in practice.

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