Multi-Objective Design Exploration for Aerodynamic Configurations

Obayashi, Shigeru; Jeong, Shinkyu; Chiba, Kazuhisa

doi:10.2514/6.2005-4666

Cited by 63 publications

(35 citation statements)

References 28 publications

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“…Having decision makers closely involved in data exploration and processing cycles eliminates the need to extract and formalize their subjective preferences. Despite the plethora of multivariate visualization aids that exist in this domain (e.g., SOM [25], Interactive Decision Maps [21], Parallel Coordinates (PC) [13]), we found no prior work on user-experience evaluation to direct our selection of a designated visual interface that would facilitate multi-objective decision making. As a result, we decided to investigate and test the effectiveness of existing visual interfaces in promoting the selection of sensible choices.…”

Section: Introductionmentioning

confidence: 92%

Evaluating Multivariate Visualizations as Multi-objective Decision Aids

Taieb-Maimon

Limonad

Amid

et al. 2013

Human-Computer Interaction – INTERACT 2013

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Abstract. When choosing a solution, decision makers are often required to account for multiple conflicting objectives. This is a situation that can result in a potentially huge number of candidate solutions. Despite the wide selection of multivariate visualizations that can potentially help decide between various candidates, no designated means exist to assess the effectiveness of these visualizations under different circumstances. As a first contribution in this work, we developed a method to evaluate different types of multivariate visualization. The method focuses on the visualization's ability to facilitate a better understanding of inter-objective trade-offs as a proxy to more sensible decision making. We used the method to evaluate two existing visualization aids: ParallelCoordinates and an adaptation of Self Organizing Maps (SOM). Both visualizations were compared with tabular data presentation. Our results show that the first visualization is more effective than a plain tabular visualization for the purpose of multi-objective decision making.

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Section: Introductionmentioning

confidence: 92%

Evaluating Multivariate Visualizations as Multi-objective Decision Aids

Taieb-Maimon

Limonad

Amid

et al. 2013

Human-Computer Interaction – INTERACT 2013

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“…Until the queue is empty, these candidate solutions are popped one by one (line [3][4][5][6][7][8][9][10][11][12][13][14]. When a solution s is obtained from the queue, it first runs a recursive Pareto improvement function (PI) at line 6.…”

Section: Qplsmentioning

confidence: 99%

“…In this case, multi-objective evolutionary algorithms (MOEAs) [2] can compute a set of solutions that approximates the Pareto front. MOEAs are general-purpose multi-objective optimization methods, and have been applied to a large variety of optimization problems [1], from reinforcement learning [15] to design space exploration [12].…”

Section: Introductionmentioning

confidence: 99%

Queued Pareto Local Search for Multi-Objective Optimization

Inja

Kooijman

Waard

et al. 2014

Parallel Problem Solving From Nature – PPSN XIII

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General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Abstract. Many real-world optimization problems involve balancing multiple objectives. When there is no solution that is best with respect to all objectives, it is often desirable to compute the Pareto front. This paper proposes queued Pareto local search (QPLS), which improves on existing Pareto local search (PLS) methods by maintaining a queue of improvements preventing premature exclusion of dominated solutions. We prove that QPLS terminates and show that it can be embedded in a genetic search scheme that improves the approximate Pareto front with every iteration. We also show that QPLS produces good approximations faster, and leads to better approximations than popular alternative MOEAs.

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“…However, the set of optimum solutions can be considered hypothetical design database. Recently, data mining technique is applied for the optimization result as the hypothetical design database to obtain the fruitful design knowledge efficiently [1,2]. As the combination between the optimization and data mining is a sequence process, it is called as MO design exploration (MODE) [3] or multidisciplinary design exploration (MDE) instead of MDO in the present study.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

Chiba

Obayashi

Morino

2008

JAMDSM

Self Cite

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Data mining is an important facet of solving multi-objective optimization problem. Because it is one of the effective manner to discover the design knowledge in the multiobjective optimization problem which obtains large data. In the present study, data mining has been performed for a large-scale and real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on the adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the tradeoffs and influence of design variables using a selforganizing map to extract key features of the design space. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.

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Multi-Objective Design Exploration for Aerodynamic Configurations

Cited by 63 publications

References 28 publications

Evaluating Multivariate Visualizations as Multi-objective Decision Aids

Evaluating Multivariate Visualizations as Multi-objective Decision Aids

Queued Pareto Local Search for Multi-Objective Optimization

Knowledge Discovery for Transonic Regional-Jet Wing through Multidisciplinary Design Exploration

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