Hierarchical distributed metamodel-assisted evolutionary algorithms in shape optimization

International Journal for Numerical Methods in Fluids

Volumn 53, Issue 3, 2007, Pages 455-469

  Karakasis, Marios K ^a   Koubogiannis, Dimitrios G ^a   Giannakoglou, Kyriakos C ^a,b  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

^b NONE   (Greece)

Author keywords

Distributed evolutionary algorithms; Hierarchical optimization; Metamodels

Indexed keywords

AIRFOILS; CASCADES (FLUID MECHANICS); COMPUTATIONAL FLUID DYNAMICS; EVOLUTIONARY ALGORITHMS; FLOW INTERACTIONS; MATHEMATICAL MODELS; NAVIER STOKES EQUATIONS; VISCOUS FLOW;

DISTRIBUTED EVOLUTIONARY ALGORITHM; FLOW ANALYSIS; HIERARCHICAL EVOLUTIONARY ALGORITHM; SHAPE OPTIMIZATION; VISCOUS-INVISCID FLOW INTERACTION;

TRANSONIC AERODYNAMICS;

AIRFOILS; CASCADES (FLUID MECHANICS); COMPUTATIONAL FLUID DYNAMICS; EVOLUTIONARY ALGORITHMS; FLOW INTERACTIONS; MATHEMATICAL MODELS; NAVIER STOKES EQUATIONS; TRANSONIC AERODYNAMICS; VISCOUS FLOW;

EID: 33846439042     PISSN: 02712091     EISSN: 10970363     Source Type: Journal    
DOI: 10.1002/fld.1288     Document Type: Article

References (40)

1. Tanese R. Distributed genetic algorithms. In Proceedings of the 3rd International Conference on Genetic Algorithms, Schaffer JD (ed.). Morgan Kaufmann Publishers Inc.: San Francisco, CA, U.S.A., 1989; 434-439.
2. Doorly DJ, Peiró J, Spooner S. Design optimisation using distributed evolutionary methods. 37th Aerospace Sciences Meeting and Exhibit, AIAA-1999-III, Reno, NV, U.S.A., January 1999.
3. Eklund SE. A massively parallel architecture for distributed genetic algorithms. Parallel Computing 2004; 30(5-6):647-676.
4. Myers RH, Montgomery DC. Response Surface Methodology: Process and Product Optimization Using Designed Experiments (2nd edn). Probability and Statistics. Wiley: New York, U.S.A., 2002.
5. Engelund WC, Stanley DO, Lepsch RA, McMillian MM, Unal R. Aerodynamic configuration design using response surface methodology analysis. Aircraft Design, Systems and Operations Meeting, AIAA-1993-3967, Monterey, CA, U.S.A., August 1993; 11-13.
6. Santner TJ, Williams BJ, Notz WI. The Design and Analysis of Computer Experiments. Statistics. Springer: New York, U.S.A., 2003.
7. Ratle A. Optimal sampling strategies for learning a fitness model. Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3. Washington DC, U.S.A., July 1999; 2078-2085.
8. Haykin S. Neural Networks: A Comprehensive Foundation (2nd edn). Prentice-Hall: Englewood Cliffs, NJ, U.S.A., 1999.
9. Greenman RM, Roth KR. Minimizing computational data requirements for multi-element airfoils using neural networks. 37th AIAA Aerospace Sciences Meeting and Exhibit, AIAA-1999-0258, Reno, NV, U.S.A., January 1999.
10. Pierret S, Van den Braembussche RA. Turbomachinery blade design using a Navier-Stokes solver and artificial neural network (ASME Paper 99-GT-4). Journal of Turbomachinery 1999; 121(2):326-332.
11. Papila N, Shyy W, Fitz-Coy N, Haftka RT. Assessment of neural net and polynomial-based techniques for aerodynamic applications. 17th AIAA Applied Aerodynamics Conference, AIAA-1999-3167, Norfolk, VA, U.S.A., 1999.
12. Giotis AP, Giannakoglou KC, Périaux J. A reduced-cost multi-objective optimization method based on the Pareto front technique, neural networks and PVM. In ECCOMAS 2000, Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering, Oñate E, Bugeda G, Suárez B (eds). CIMNE: Barcelona, Spain, 2000.
13. Nakayama H, Arakawa M, Washino K. Using support vector machines in optimization for black-box objective functions. Proceedings of the International Joint Conference on Neural Networks, July 2003, vol. 2. 2003; 1617-1622.
14. Carpenter WC, Barthelemy J-FM. A comparison of polynomial approximations and artificial neural nets as response surfaces. Structural and Multidisciplinary Optimization 1993; 5(3):166-174.
15. Simpson TW, Mauery TM, Korte JJ, Mistree F. Comparison of response surface and kriging models for multidisciplinary design optimization. Proceedings of the 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, AIAA-1998-4758. St. Louis, MO, U.S.A., September 1998.
16. Shyy W, Papila N, Vaidyanathan R, Tucker K. Global design optimization for aerodynamics and rocket propulsion components. Progress in Aerospace Sciences 2001; 37(1):59-118.
17. Papadrakakis M, Lagaros ND, Tsompanakis Y. Structural optimization using evolution strategies and neural networks. Computer Methods in Applied Mechanics and Engineering 1998; 156(1-4):309-333.
18. Bull L. On model-based evolutionary computation. Soft Computing - A Fusion of Foundations, Methodologies and Applications 1999; 3(2):76-82.
19. Nakayama H, Inoue K, Yoshimori Y. Approximate optimization using computational intelligence and its application to reinforcement of cable-stayed bridges. In ECCOMAS 2004, Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering, Neittaanmäki P, Rossi T, Korotov S, Oñate E, Périaux J, Knörzer D (eds). Jÿvaskÿla, Finland, July 2004.
20. Lagaros ND, Charmpis DC, Papadrakakis M. An adaptive neural network strategy for improving the computational performance of evolutionary structural optimization. Computer Methods in Applied Mechanics and Engineering 2005; 194(30-33):3374-3393.
21. Büche D, Schraudolph N, Koumoutsakos P. Accelerating evolutionary algorithms with Gaussian process fitness function models. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 2005; 35(2):183-194.
22. Jin Y, Olhofer M, Sendhoff B. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation 2002; 6(5):481-494.
23. Giannakoglou KC. Designing turbomachinery blades using evolutionary methods. ASME Turbo Expo'99, ASME Paper 99-GT-181. Indianapolis, U.S.A., June 1999.
24. Ong YS, Lum KY, Nair PB, Shi DM, Zhang ZK. Global convergence of unconstrained and bound constrained surrogate-assisted evolutionary search in aerodynamic shape design. Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03), vol. 3. Canberra, Australia, December 2003; 1856-1863.
25. Ulmer H, Streichert F, Zell A. Evolution strategies assisted by Gaussian processes with improved pre-selection criterion. Proceedings of the 2003 Congress on Evolutionary Computation (CEC'03), vol. 1. Canberra, Australia, 2003; 692-699.
26. Branke J, Schmidt C. Faster convergence by means of fitness estimation. Soft Computing - A Fusion of Foundations, Methodologies and Applications 2005; 9(1):13-20.
27. Giannakoglou KC, Giotis AP, Karakasis MK. Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters. Journal of Inverse Problems in Engineering 2001; 9(4):389-412.
28. Karakasis MK, Giotis AP, Giannakoglou KC. Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization. International Journal for Numerical Methods in Fluids 2003; 43(10-11):1149-1166.
29. Sefrioui M, Périaux J. A hierarchical genetic algorithm using multiple models for optimization. In Proceedings of the 6th International Conference on Parallel Problem Solving from Nature-PPSN VI, Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Guervós JJM, Schwefel H-P (eds), Lecture Notes in Computer Science, vol. 1917. Springer: New York, Paris, France, 2000; 879-888.
30. Eby D, Averill RC, Punch III WF, Goodman ED. Evaluation of injection island GA performance on flywheel design optimization. Proceedings of the 3rd Conference on Adaptive Computing in Design and Manufacturing, Plymouth, U.K., 1998; 121-136.
31. Dulikravich GS. Shape inverse design and optimization for three-dimensional aerodynamics. 33rd Aerospace Sciences Meeting and Exhibit, AIAA-1995-695, Reno, NV, U.S.A., January 1995.
32. Foster NF, Dulikravich GS. Three-dimensional aerodynamic shape optimisation using genetic and gradient search algorithms. Journal of Spacecraft and Rockets 1997; 34(1):36-42.
33. Karakasis MK, Giannakoglou KC. On the use of surrogate evaluation models in multi-objective evolutionary algorithms. In ECCOMAS 2004, Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering, Neittaanmäki P, Rossi T, Korotov S, Oñate E, Périaux J, Knörzer D (eds). Jÿvaskÿla, Finland, July 2004.
34. Giles MB, Drela M. A two-dimensional transonic aerodynamic design method. AIAA Journal 1987; 25(9):1199-1206.
35. Drela M, Giles MB. Viscous-inviscid analysis of transonic and low Reynolds number airfoils. AIAA Journal 1987; 25(10):1347-1355.
36. Koubogiannis DG, Athanassiadis AN, Giannakoglou KC. One- and two-equation turbulence models for the prediction of complex cascade flows using unstructured grids. Computers & Fluids 2003; 32(3):403-430.
37. Poggio T, Girosi F. Networks for approximation and learning. Proceedings of the IEEE 1990; 78(9):1481-1497.
38. Park J, Sandberg IW. Universal approximation using radial-basis-function networks. Neural Computation 1991; 3(2):246-257.
39. Cook PH, McDonald MA, Firmin MC. Airfoil RAE 2822 - pressure distributions and boundary layer and wake measurements. AGARD-AR-138 (Experimental Data Base for Computer Program Assessment), May 1979.
40. Steinert W, Eisenberg B, Starken H. Design and testing of a controlled diffusion airfoil cascade for industrial axial flow compressor application. Gas Turbine and Aeroengine Congress and Exposition, ASME Paper 90-GT-140, Brussels, Belgium, June 1990.