Evaluating the Performance of Visual Steering Commands for User-Guided Pareto Frontier Sampling During Trade Space Exploration

2008 ◽  
Author(s):  
Dan Carlsen ◽  
Matthew Malone ◽  
Josh Kollat ◽  
Timothy W. Simpson
Keyword(s):  
Decision Making ◽  
Design Optimization ◽  
Optimization Problems ◽  
Space Exploration ◽  
Pareto Frontier ◽  
Optimization Process ◽  
Multi Objective Genetic Algorithm ◽  
Trade Space Exploration ◽  
Support Engineering ◽  
Visualization Techniques

Trade space exploration is a promising decision-making paradigm that provides a visual and more intuitive means for formulating, adjusting, and ultimately solving design optimization problems. This is achieved by combining multi-dimensional data visualization techniques with visual steering commands to allow designers to “steer” the optimization process while searching for the best, or Pareto optimal, designs. In this paper, we compare the performance of different combinations of visual steering commands implemented by two users to a multi-objective genetic algorithm that is executed “blindly” on the same problem with no human intervention. The results indicate that the visual steering commands — regardless of the combination in which they are invoked — provide a 4x–7x increase in the number of Pareto solutions that are obtained when the human is “in-the-loop” during the optimization process. As such, this study provides the first empirical evidence of the benefits of interactive visualization-based strategies to support engineering design optimization and decision-making. Future work is also discussed.

Introduction of a Tradeoff Index for Efficient Trade Space Exploration

2015 ◽  
Author(s):  
Mehmet Unal ◽  
Gordon Warn ◽  
Timothy W. Simpson
Keyword(s):  
Approximate Solution ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Space Exploration ◽  
High Dimensional ◽  
Two Dimensional ◽  
Complex Design ◽  
Trade Space Exploration ◽  
Tradeoff Index

The development of many-objective evolutionary algorithms has facilitated solving complex design optimization problems, that is, optimization problems with four or more competing objectives. The outcome of many-objective optimization is often a rich set of solutions, including the non-dominated solutions, with varying degrees of tradeoff amongst the objectives, herein referred to as the trade space. As the number of objectives increases, exploring the trade space and identifying acceptable solutions becomes less straightforward. Visual analytic techniques that transform a high-dimensional trade space into two-dimensional (2D) presentations have been developed to overcome the cognitive challenges associated with exploring high-dimensional trade spaces. Existing visual analytic techniques either identify acceptable solutions using algorithms that do not allow preferences to be formed and applied iteratively, or they rely on exhaustive sets of 2D representations to identify tradeoffs from which acceptable solutions are selected. In this paper, an index is introduced to quantify tradeoffs between any two objectives and integrated into a visual analytic technique. The tradeoff index enables efficient trade space exploration by quickly pinpointing those objectives that have tradeoffs for further exploration, thus reducing the number of 2D representations that must be generated and interpreted while allowing preferences to be formed and applied when selecting a solution. Furthermore, the proposed index is scalable to any number of objectives. Finally, to illustrate the utility of the proposed tradeoff index, a visual analytic technique that is based on this index is applied to a Pareto approximate solution set from a design optimization problem with ten objectives.

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.

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

Visual Steering Commands for Trade Space Exploration: User-Guided Sampling With Example

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Gary Stump ◽  
Sara Lego ◽  
Mike Yukish ◽  
Timothy W. Simpson ◽  
Joseph A. Donndelinger
Keyword(s):  
Design Space ◽  
Space Exploration ◽  
Pareto Frontier ◽  
Configuration Model ◽  
Future Research ◽  
Scatter Matrix ◽  
Computing Power ◽  
Point Of Interest ◽  
High Preference ◽  
Trade Space Exploration

Recent advancements in computing power and speed provide opportunities to revolutionize trade space exploration, particularly for the design of complex systems such as automobiles, aircraft, and spacecraft. In this paper, we introduce three visual steering commands to support trade space exploration and demonstrate their use within a powerful data visualization tool that allows designers to explore multidimensional trade spaces using glyph, 1D and 2D histograms, 2D scatter, scatter matrix, and parallel coordinate plots, linked views, brushing, preference shading, and Pareto frontier display. In particular, we define three user-guided samplers that enable designers to explore (1) the entire design space, (2) near a point of interest, or (3) within a region of high preference. We illustrate these three samplers with a vehicle configuration model that evaluates the technical feasibility of new vehicle concepts. Future research is also discussed.

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.

scholarly journals Multi-Objective Design Optimization of a Variable Geometry Spray Fuel Injector

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
J. R. Archer ◽  
Tiegang Fang ◽  
Scott Ferguson ◽  
Gregory D. Buckner
Keyword(s):  
Design Optimization ◽  
High Performance ◽  
Pareto Frontier ◽  
Spray Angle ◽  
Design Parameters ◽  
Variable Geometry ◽  
Fuel Injector ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

This paper explores the simulation-based design optimization of a variable geometry spray (VGS) fuel injector. A multi-objective genetic algorithm (MOGA) is interfaced with commercial computational fluid dynamics (CFD) software and high performance computing capabilities to evaluate the spray characteristics of each VGS candidate design. A three-point full factorial experimental design is conducted to identify significant design variables and to better understand possible variable interactions. The Pareto frontier of optimal designs reveals the inherent tradeoff between two performance objectives—actuator stroke and spray angle sensitivity. Analysis of these solutions provides insight into dependencies between design parameters and the performance objectives and is used to assess possible performance gains with respect to initial prototype configurations. These insights provide valuable design information for the continued development of this VGS technology.

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