Minimal Representation of Multiobjective Design Space Using a Smart Pareto Filter. Mattson, C., Mullur, A., & Messac, A. In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, pages AIAA-2002-5458, 9, 2002. American Institute of Aeronautics and Astronautics. ![Minimal Representation of Multiobjective Design Space Using a Smart Pareto Filter [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
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Website abstract bibtex * Multiobjective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solving multiobjective optimization problems is to first generate a discrete set of Pareto solutions from which the designer chooses the most desirable. A Pareto solution is one where any improvement in one objective results in the worsening of at least one other objective. These solutions are therefore the ones that we generally seek in any practical setting. The success of this approach critically depends on our ability to obtain, manage, and interpret the Pareto set; and, importantly, on the size and distribution of the Pareto set. To improve decision-making, the discrete set under consideration should be adequately small, and should meaningfully represent the complete/continuous Pareto frontier. Ideally, the discrete set should emphasize the regions of the Pareto set that entail significant tradeoff, and deemphasize the regions of little tradeoff. No current Pareto set generation method systematically possesses these important and desirable properties. We call a smart Pareto set a discrete Pareto set that (i) is small and (ii) effectively represents the tradeoff properties of the Pareto frontier. This paper presents a general method to obtain smart Pareto sets in n-dimension. The smallness of the set and the desired degree of tradeoff among objectives in the resulting set are controlled by the designer. The method presented in this paper is based on the development of a so-called smart Pareto filter. Mathematical and physical examples are provided to illustrate the effectiveness of the method.
@inProceedings{
title = {Minimal Representation of Multiobjective Design Space Using a Smart Pareto Filter},
type = {inProceedings},
year = {2002},
identifiers = {[object Object]},
pages = {AIAA-2002-5458},
websites = {http://arc.aiaa.org/doi/10.2514/6.2002-5458},
month = {9},
publisher = {American Institute of Aeronautics and Astronautics},
day = {4},
city = {Reston, Virigina},
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last_modified = {2017-03-14T10:29:11.151Z},
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abstract = {* Multiobjective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solving multiobjective optimization problems is to first generate a discrete set of Pareto solutions from which the designer chooses the most desirable. A Pareto solution is one where any improvement in one objective results in the worsening of at least one other objective. These solutions are therefore the ones that we generally seek in any practical setting. The success of this approach critically depends on our ability to obtain, manage, and interpret the Pareto set; and, importantly, on the size and distribution of the Pareto set. To improve decision-making, the discrete set under consideration should be adequately small, and should meaningfully represent the complete/continuous Pareto frontier. Ideally, the discrete set should emphasize the regions of the Pareto set that entail significant tradeoff, and deemphasize the regions of little tradeoff. No current Pareto set generation method systematically possesses these important and desirable properties. We call a smart Pareto set a discrete Pareto set that (i) is small and (ii) effectively represents the tradeoff properties of the Pareto frontier. This paper presents a general method to obtain smart Pareto sets in n-dimension. The smallness of the set and the desired degree of tradeoff among objectives in the resulting set are controlled by the designer. The method presented in this paper is based on the development of a so-called smart Pareto filter. Mathematical and physical examples are provided to illustrate the effectiveness of the method.},
bibtype = {inProceedings},
author = {Mattson, Christopher and Mullur, Anoop and Messac, Achille},
booktitle = {9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization}
}
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