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TECHNICAL PAPERS

Interactive Multiobjective Optimization Design Strategy for Decision Based Design

[+] Author and Article Information
Ravindra V. Tappeta, John E. Renaud

Aerospace and Mechanical Engineering University of Notre Dame Notre Dame, IN 46556

J. Mech. Des 123(2), 205-215 (Feb 01, 1999) (11 pages) doi:10.1115/1.1358302 History: Received February 01, 1999
Copyright © 2001 by ASME
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References

Eschenauer,  H. A., Geilen,  J., and Wahl,  H. J., 1993, “SAPOP—an Optimization Procedure for Multicriteria Structural Design,” International Series of Numerical Mathematics, 110, pp. 207–227.
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Stadler,  W., 1995, “Caveats and Boons of Multicriteria Optimization,” Microcomputers in Civil Engineering, 10, pp. 291–299.
Hwang, C. L., and Masud, A. S. M., 1979, Multiple Objective Decision Making-Methods and Applications, Springer-Verlag, Berlin.
Tappeta,  R. V., and Renaud,  J. E., 1999, “Interactive MultiObjective Optimization Procedure,” AIAA J., 37, No. 7, July, pp. 881–889.
Tappeta,  R. V., and Renaud,  J. E., 2000, “Interactive Physical Programming: Tradeoff Analysis and Decision Making,” AIAA J. , 38, No. 5, May, pp. 917–926.
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Miettinen, K., 1998, Nonlinear Multiobjective Optimization, Kluwer Academic, Boston.
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Gabriele, G. A., and Beltracchi, T. J., 1988, “OPT3.2: A Fortran Implementation of the Generalized Reduced Gradient Method,” Users Manual, Department of Mechanical Engineering, Aerospace Engineering and Mechanical, Rensselaer Polytechnic Institute.
Roy,  B., and Vanderpooten,  D., 1996, “The European School of MCDA: Emergence, Basic Features and Current Works,” Journal of Multi-Criteria Decision Analysis, 5, pp. 22–38.
The MathWorks, Inc., 1992, MATLAB Reference Guide, August.
Grace, A., 1992, Optimization Toolbox for use with MATLAB, The Math Works, Inc., Natick, MA
Wujek, B. A., Renaud, J. E., and Brockman, J. B., 1994, “Design Driven Concurrent Optimization in System Design Problems Using Second Order Sensitivities,” AIAA 94-4276-CP, Proceedings of the 5th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Panama City, Florida, September 7–9.

Figures

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Flowchart of the Iterative Decision Making strategy (IDMS)
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Projecting the aspiration point f̄1 onto the Pareto curve
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Illustration of avoiding non-Pareto regions
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The flowchart of iMOODS
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Pareto curve approximations
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Avoiding the data generation in non-Pareto region
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High-performance low-cost structure
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Prediction errors in f3 (scenario—1)
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Prediction errors in f3 (scenario—2)

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