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TECHNICAL PAPERS

Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set

[+] Author and Article Information
Jin Wu, Shapour Azarm

  Department of Mechanical Engineering, University of Maryland, College Park, MD 20742

J. Mech. Des 123(1), 18-25 (Jan 01, 2000) (8 pages) doi:10.1115/1.1329875 History: Received January 01, 2000
Copyright © 2001 by ASME
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References

Eschenauer, C. M., Koski, J., and Osyczka, A., eds., 1990, Multicriteria Design Optimization, Springer-Verlag, New York.
Miettinen, K. M., 1999, Nonlinear Multiobjective Optimization, Kluwer Academic, Boston.
Bäck, T., 1996, Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York.
Zitzler, E., and Thiele, L., 1998, “Multiobjective Optimization Using Evolutionary Algorithms—A Comparative Case Study,” In Eiben, A. E., et al., Proc. 5th International Conference: Parallel Problem Solving from Nature—PPSNV, Amsterdam, The Netherlands, Springer, pp. 292–301.
Van Veldhuizen, D. A., 1999, “Multiobjective Evolutionary Algorithm: Classifications, Analyses, and New Innovations,” Ph.D. Dissertation, Department of Electrical and Computer Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio.
Schott, J. R., 1995, “Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization,” MS Thesis, Department of Aeronautics and Astronautics, MIT, Cambridge, Massachusetts.
Srinivas,  N., and Deb,  K., 1994, “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithm,” Evolu. Comput. ,2, No. 3, pp. 221–248.
Sayin, S., 1997, “Measuring the Quality of Discrete Representations of Efficient Sets in Multiple Objective Mathematical Programming,” Working Paper No. 1997/25, Koç University, Turkey.
Messac,  A., 1996, “Physical Programming: Effective Optimization for Computational Design,” AIAA J., 34, No. 1, pp. 149–158.
Azarm, S., Reynolds, B., and Narayanan, S., 1999, “Comparison of Two Multiobjective Optimization Techniques with and within Genetic Algorithm,” CD-ROM Proceedings of the ASME DETC, Design Automation Conference, Paper No. DETC99/DAC-8584, DETC’99 September 12–16, 1999, Las Vegas, Nevada.

Figures

Grahic Jump Location
Good point (pg), bad point (pb), ideal point (pI), and max point (pM) in the objective space
Grahic Jump Location
Dominant, inferior, and non-inferior regions of a point
Grahic Jump Location
Dominant, inferior, non-inferior, and observed pareto frontier regions of a set P
Grahic Jump Location
Overall pareto spread and kth pareto spread
Grahic Jump Location
Indifference region Tμ(q), as shown by a shaded grid
Grahic Jump Location
Vibrating platform example
Grahic Jump Location
Two sets of observed Pareto solutions

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