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.

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.

Toward a Value-Driven Design Approach for Complex Engineered Systems Using Trade Space Exploration Tools

2014 ◽  
Author(s):  
Simon W. Miller ◽  
Timothy W. Simpson ◽  
Michael A. Yukish ◽  
Gary Stump ◽  
Bryan L. Mesmer ◽  
...  
Keyword(s):  
Data Visualization ◽  
Space Exploration ◽  
Systems Design ◽  
Design Decision ◽  
Value Functions ◽  
Complex Engineered Systems ◽  
A Value ◽  
Trade Offs ◽  
Engineered Systems ◽  
Trade Space Exploration

Design decision-making involves trade-offs between many design variables and attributes, which can be difficult to model and capture in complex engineered systems. To choose the best design, the decision-maker is often required to analyze many different combinations of these variables and attributes and process the information internally. Trade Space Exploration (TSE) tools, including interactive and multi-dimensional data visualization, can be used to aid in this process and provide designers with a means to make better decisions, particularly during the design of complex engineered systems. In this paper, we investigate the use of TSE tools to support decision-makers using a Value-Driven Design (VDD) approach for complex engineered systems. A VDD approach necessitates a rethinking of trade space exploration. In this paper, we investigate the different uses of trade space exploration in a VDD context. We map a traditional TSE process into a value-based trade environment to provide greater decision support to a design team during complex systems design. The research leverages existing TSE paradigms and multi-dimensional data visualization tools to identify optimal designs using a value function for a system. The feasibility of using these TSE tools to help formulate value functions is also explored. A satellite design example is used to demonstrate the differences between a VDD approach to design complex engineered systems and a multi-objective approach to capture the Pareto frontier. Ongoing and future work is also discussed.

Toward a visual approach in the exploration of shape grammars

2015 ◽  
Vol 29 (4) ◽  
pp. 503-521 ◽  
Author(s):  
Tiemen Strobbe ◽  
Pieter Pauwels ◽  
Ruben Verstraeten ◽  
Ronald De Meyer ◽  
Jan Van Campenhout
Keyword(s):  
Design Process ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Shape Grammar ◽  
Creative Design ◽  
Software System ◽  
Shape Grammars ◽  
Support Design ◽  
Exploration Process

AbstractThe concept of shape grammars has often been proposed to improve or support creative design processes. Shape grammar implementations have the potential to both automate parts of the design process and allow exploration of design alternatives. In many of the existing implementations, the main focus is either on capturing the rationale of a particular existing grammar or on allowing designers to develop a new grammar. However, little attention is typically given to the actual representation of the design space that can be explored in the interface of the implementation. With such representation, a shape grammar implementation could properly support designers who are still in the process of designing and may not yet have a clear shape grammar in mind. In this article, an approach and a proof-of-concept software system is proposed for a shape grammar implementation that provides a visual and interactive way to support design space exploration in a creative design process. We describe the method by which this software system can be used and focus on how designers can interact with the exploration process. In particular, we point out how the proposed approach realizes several important amplification strategies to support design space exploration.

Quantifying the Shape of a Pareto Front in Support of Many-Objective Trade Space Exploration

2016 ◽  
Author(s):  
Mehmet Unal ◽  
Gordon P. Warn ◽  
Timothy W. Simpson
Keyword(s):  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Space Exploration ◽  
Shape Index ◽  
Analytical Geometry ◽  
Conflicting Objectives ◽  
Diminishing Returns ◽  
Final Design ◽  
Trade Space Exploration

Complex design optimization problems typically include many conflicting objectives, and the resulting trade space is comprised of numerous design solutions. To efficiently explore a many-objective trade space, form preferences, and select a final design, one must identify and negotiate tradeoffs between multiple, often conflicting, objectives. Identifying conflicting objective pairs allows decision-makers to concentrate on these objectives when selecting preferred designs from the non-dominated solution set, i.e., the Pareto front. Techniques exist to identify and visualize tradeoffs between these conflicting objectives to support trade space exploration; however, these techniques do not quantify, or differentiate, the shape of the Pareto front, which might be useful information for a decision-maker. More specifically, designers could gain insight from the degree of diminishing returns among solutions on the Pareto front, which can be used to understand the extent of the tradeoffs in the problem. Therefore, the shape of the Pareto front could be used to prioritize exploration of conflicting objective pairs. In this paper, we introduce a novel index that quantifies the shape of the Pareto front to provide information about the degree of diminishing returns. The aim of the index is to help designers gain insight into the underlying tradeoffs in a many-objective optimization problem and support trade space exploration by prioritizing the negotiation of conflicting objectives. The proposed Pareto Shape Index is based on analytical geometry and derived from the coordinates of the Pareto solutions in the n objective trade space. The utility of the Pareto Shape Index in differentiating diminishing returns between conflicting objectives is demonstrated by application to an eight-objective benchmark optimization problem.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

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