Data processing: structural design – modeling – simulation – and em – Modeling by mathematical expression
Reexamination Certificate
2006-08-18
2011-10-18
Shah, Kamini S (Department: 2128)
Data processing: structural design, modeling, simulation, and em
Modeling by mathematical expression
Reexamination Certificate
active
08041545
ABSTRACT:
Concurrent Gradients Analysis (CGA), and two multi-objective optimization methods based on CGA are provided: Concurrent Gradients Method (CGM), and Pareto Navigator Method (PNM). Dimensionally Independent Response Surface Method (DIRSM) for improving computational efficiency of optimization algorithms is also disclosed. CGM and PNM are based on CGA's ability to analyze gradients and determine the Area of Simultaneous Criteria Improvement (ASCI). CGM starts from a given initial point, and approaches the Pareto frontier sequentially stepping into the ASCI area until a Pareto optimal point is obtained. PNM starts from a Pareto-optimal point, and steps along the Pareto surface in the direction that allows improving a subset of objective functions with higher priority. DIRSM creates local approximations based on automatically recognizing the most significant design variables. DIRSM works for optimization tasks with virtually any (small or large) number of design variables, and requires just 2-3 model evaluations per Pareto optimal point for the CGM and PNM algorithms.
REFERENCES:
patent: 7295956 (2007-11-01), Ruetsch
patent: 7593834 (2009-09-01), Levitan et al.
patent: 2005/0187844 (2005-08-01), Chalermkraivuth et al.
patent: 2006/0033989 (2006-02-01), Chugh et al.
Kim et al., “Adaptive Weighted Sum Method for Multiobjective Optimization”, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2004, 13 pages.
Abbass et al., “PDE: A Pareto-Frontier Differential Evolution Approach for Multi-Objective Optimization Problems”, Proceedings of the 2001 Congress on Evolutionary Computation, 2001, pp. 971-978.
Andersson, J., “A Survey of Multi-Optimization in Engineering Design”, Fluid and Mechanical Engineering Systems, Linkoping University, Sweden, LiTH-IKP-R1097, 1997.
Marler, R.T. and Arora, J.S. (2004) Survey of Multi-Objective Optimization Methods for Engineering, Structural and Multidisciplinary Optimization, 26, 6, 369-395.
Pareto, V. 1906: Manuale di Economica ,Politica, Societa Editrice Libraria. Milan; translated into English by A.S. Schwier as Manual of Political Economy. Edited by A.S. Schwier and A.N. Page, 1971. New York: A.M. Kelley.
Holland, J.H. (1975). Adaption in Natural and Artificial Systems. University of Michigan Press: Ann Arbor, MI.
Vanderplaats, G.N., Numerical Optimization Techniques for Engineering Design, McGraw-Hill Book Co., 1984.
Haftka, R.T. and Gurdal, Z., Elements of Structural Optimization, Kluwer Academic Publishers, 1992.
Walsh, G.R., Methods of Optimization, John Wiley 1975.
Weiyu Liu, Development of Gradient-Enhanced Kriging Approximations for Multidisciplinary Design Optimization, Dissertation, University of Notre Dame, 2003.
Simpson, T.W., Peplinski, J., Koch, P.N. and Allen, J.K., Metamodels For Computer-Based Engineering Design: Survey and Recommendations, Engineering with Computers, vol. 17, No. 2 2001, pp. 129-150.
Balabanov, V., Venter G., Multi-Fidelity Optimization With High-Fidelity Analysis and Low-Fidelity Gradients, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, New York, Aug. 30-1, 2004.
M.C. Fu, Optimization For Simulation: Theory vs. Practice, INFORMS Journal on Computing, 2002.
Simpson,.T.W., Booker, A.J., Ghosh, D., Giunta, A.A., Koch, P.N. and Yang, R.-J (2004), Approximation Methods In Multidisciplinary Analysis and Optimization: A Panel Discussion, Structural and Multidisciplinary Optimization 27:5 (302-313).
Box, G.E.P. and Draper, N. R., Evolutionary Operation: A Statistical Method For Process Management, John Wiley & Sons, Inc., New York 1969.
Balabanov, V.O., Giunta, A.A., Golovidov, O., Grossman, B., Mason, W.H. and Watson, L.T., Reasonable Design Space Approach To Response Surface Approximation, Journal of Aircraft, vol. 36, No. 1, 1999, pp. 308-315.
Venter, G., Haftka, R., Starnes, J., Construction Of Response Surface Approximations For Design Optimization. AIAA Journal 36:2242-2249, 1998. P. Vincent and Y. Bengio.
Sevastyanov Vladimir
Shaposhnikov Oleg
Day Herng-Der
Shah Kamini S
Tachner Leonard
LandOfFree
Gradient based methods for multi-objective optimization does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Gradient based methods for multi-objective optimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Gradient based methods for multi-objective optimization will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-4296024