A grid-enabled asynchronous metamodel-assisted evolutionary algorithm for aerodynamic optimization

Genetic Programming and Evolvable Machines

Volumn 10, Issue 4, 2009, Pages 373-389

  Asouti, V G ^a   Kampolis, I C ^a   Giannakoglou, K C ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Aerodynamic shape optimization; Asynchronous evolutionary algorithms; Grid computing; Metamodels

Indexed keywords

AERODYNAMIC OPTIMIZATION; AERODYNAMIC SHAPE OPTIMIZATION; ARTIFICIAL NEURAL NETWORK; ASYNCHRONOUS EVOLUTIONARY ALGORITHMS; COMPRESSOR CASCADE; EVALUATION MODELS; GLOBUS TOOLKIT; GRID DEPLOYMENT; META MODEL; METAMODELS; MIDDLEWARE LAYER; MULTI OBJECTIVE; PRE-EVALUATION; SEARCH METHOD;

AERODYNAMICS; AIRFOILS; COMPUTATION THEORY; GRID COMPUTING; MIDDLEWARE; NEURAL NETWORKS; SHAPE OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 70449534815     PISSN: 13892576     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10710-009-9090-5     Document Type: Article

References (42)

1. E. Alba M. Tomassini 2002 Parallelism and evolutionary algorithms IEEE Trans. Evol. Comput. 6 443 462 10.1109/TEVC.2002.800880 (Pubitemid 35327563)
2. V.G. Asouti K.C. Giannakoglou 2009 Aerodynamic optimization using a parallel asynchronous evolutionary algorithm controlled by strongly interacting demes Eng. Optim. 41 3 241 257 10.1080/03052150802415665 2511064
3. J. Branke C. Schmidt 2005 Faster convergence by means of fitness estimation Soft Comput. Fusion Found. Methodol. Appl. 9 1 13 20
4. D. Büche N. Schraudolph P. Koumoutsakos 2005 Accelerating evolutionary algorithms with Gaussian process fitness function models IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 35 2 183 194 10.1109/TSMCC.2004. 841917
5. L. Bull 1999 On model-based evolutionary computation Soft Comput. Fusion Found. Methodol. Appl. 3 2 76 82 10.1007/s005000050055
6. E. Cantú-Paz 1998 A survey of parallel genetic algorithms Calculateurs Paralleles Reseaux et Systemes Repartis 10 141 171
7. D.J. Doorly, J. Peiró, S. Spooner, Design optimisation using distributed evolutionary methods, in 37th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA. AIAA-1999-111 (1999)
8. M. Emmerich, A. Giotis, M. Özdemir, T. Bäck, K.C. Giannakoglou, Metamodel-assisted evolution strategies, in Parallel Problem Solving from Nature-PPSN VII. (Springer, Granada, 2002), pp. 361-370
9. I. Foster, Globus toolkit version 4: software for service-oriented systems, in IFIP International Conference on Network and Parallel Computing, vol. 3779 (Springer, Berlin Heidelberg, 2006), pp. 2-13
10. C.A. Georgopoulou, K.C. Giannakoglou, A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement. Eng. Optim. 41(10), 909-923 (2009)
11. K.C. Giannakoglou, Designing turbomachinery blades using evolutionary methods. ASME paper 99-GT-181, 44th ASME Gas Turbine & Aeroengine Congress, Indianapolis, IN, USA, June (1999)
12. K.C. Giannakoglou 2002 Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence Prog. Aerosp. Sci. 38 1 43 76 10.1016/S0376-0421(01)00019-7 (Pubitemid 34201146)
13. K.C. Giannakoglou A.P. Giotis M.K. Karakasis 2001 Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters Inverse Probl. Eng. 9 389 412 10.1080/174159701088027771
14. S. Haykin, Neural Networks: A Comprehensive Foundation (Prentice Hall, New Jersey, 1999)
15. Y. Jin M. Olhofer B. Sendhoff 2002 A framework for evolutionary optimization with approximate fitness functions IEEE Trans. Evol. Comput. 6 5 481 494 10.1109/TEVC.2002.800884 (Pubitemid 35327564)
16. I.C. Kampolis K.C. Giannakoglou 2008 A multilevel approach to single- and multiobjective aerodynamic optimization Comput. Methods Appl. Mech. Eng. 197 33-40 2963 2975 10.1016/j.cma.2008.01.015
17. I.C. Kampolis, A.S. Zymaris, V.G. Asouti, K.C. Giannakoglou, Multilevel optimization strategies based on metamodel-assisted evolutionary algorithms, for computationally expensive problems, in 2007 Congress on Evolutionary Computation-CEC '07 (Singapore, 2007)
18. M.K. Karakasis K.C. Giannakoglou 2006 On the use of metamodel-assisted, multi-objective evolutionary algorithms Eng. Optim. 38 8 941 957 10.1080/03052150600848000 2266109 (Pubitemid 44627566)
19. M.K. Karakasis A.P. Giotis K.C. Giannakoglou 2003 Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization Int. J. Numer. Methods Fluids 43 10-11 1149 1166 1032.76654 10.1002/fld.575
20. M.K. Karakasis D.G. Koubogiannis K.C. Giannakoglou 2007 Hierarchical distributed evolutionary algorithms in shape optimization Int. J. Numer. Methods Fluids 53 3 455 469 1107.65056 10.1002/fld.1288 (Pubitemid 46139528)
21. P.I.K. Liakopoulos I.C. Kampolis K.C. Giannakoglou 2008 Grid-enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization Future Gener. Comput. Syst. 24 7 701 708 10.1016/j.future.2008.03.004
22. D. Lim Y.-S. Ong Y. Jin B. Sendhoff B.-S. Lee 2007 Efficient hierarchical parallel genetic algorithms using grid computing Future Gener. Comput. Syst. 23 4 658 670 10.1016/j.future.2006.10.008 (Pubitemid 46185695)
23. F. Luna A.J. Nebro E. Alba 2006 Observations in using grid-enabled technologies for solving multi-objective optimization problems Parallel Comput. 32 5-6 377 393 10.1016/j.parco.2006.06.004 2253743 (Pubitemid 44279880)
24. Y. Mack, T. Goel, W. Shyy, R. Haftka, Surrogate model-based optimization framework: a case study in aerospace design, in Evolutionary Computation in Dynamic and Uncertain Environments, ed. by Y.-S. Ong, Y. Jin (Springer, Singapore, 2007)
25. K. Mathioudakis, K.D. Papailiou, N. Neris, C. Bonhommet, G. Albrand, U. Wenger, An annular cascade facility for studying tip clearance effects in high speed flows, in XIII ISABE Conference (Chattanooga, Tennessee, USA, 1997)
26. N. Melab S. Cahon E.-G. Talbi 2006 Grid computing for parallel bioinspired algorithms J. Parallel Distrib. Comput. 66 8 1052 1061 1103.68977 10.1016/j.jpdc.2005.11.006 (Pubitemid 44026579)
27. R.S. Montero E. Huedo I.M. Llorente 2004 A framework for adaptive execution in grids J. Softw. Pract. Exp. 34 7 631 651 10.1002/spe.584
28. A.J. Nebro E. Alba F. Luna 2007 Multi-objective optimization using grid computing Soft Comput. 11 6 531 540 10.1007/s00500-006-0096-0
29. M. Nowostawski, R. Poli, Parallel genetic algorithm taxonomy, in Third International Conference on Knowledge-based Intelligent Information Engineering Systems KES'99 (1999)
30. Y.-S. Ong P.B. Nair A.J. Keane 2003 Evolutionary optimization of computationally expensive problems via surrogate modeling AIAA J. 41 4 687 696 10.2514/2.1999
31. D.I. Papadimitriou K.C. Giannakoglou 2007 A continuous adjoint method with objective function derivatives based on boundary integrals for inviscid and viscous flows Comput. Fluids 36 2 325 341 10.1016/j.compfluid.2005.11.006 (Pubitemid 44689205)
32. D.I. Papadimitriou K.C. Giannakoglou 2007 Total pressure loss minimization in turbomachinery cascades using a new continuous adjoint formulation Proc. I MECH E Part A J. Power Energy 221 865 872 10.1243/09576509JPE463 (Pubitemid 47603761)
33. M. Papadrakakis N.D. Lagaros Y. Tsompanakis 1998 Structural optimization using evolution strategies and neural networks Comput. Methods Appl. Mech. Eng. 156 1-4 309 333 0964.74045 10.1016/S0045-7825(97)00215-6 (Pubitemid 128396491)
34. S. Pierret R.A. Van den Braembussche 1999 Turbomachinery blade design using a Navier-Stokes solver and artificial neural network J. Turbomach. 121 2 326 332 10.1115/1.2841318
35. C. Poloni A. Giurgevich L. Onesti V. Pediroda 2000 Hybridization of a multiobjective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics Comput. Methods Appl. Mech. Eng. 186 2 403 420 0956.76023 10.1016/S0045-7825(99)00394-1 (Pubitemid 30416744)
36. T. Ray W. Smith 2006 A surrogate assisted parallel multiobjective evolutionary algorithm for robust engineering design Eng. Optim. 38 8 997 1011 10.1080/03052150600882538 (Pubitemid 44627569)
37. P. Spalart S. Allmaras 1994 A one-equation turbulence model for aerodynamic flows La Recherche Aerospatiale 1 5 21
38. R. Storn K. Price 1997 Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces J. Glob. Optim. 11 4 341 359 0888.90135 10.1023/A:1008202821328 1479553
39. D. Thain T. Tannenbaum M. Livny 2005 Distributed computing in practice: the Condor experience Concurr. Pract. Exp. 17 2-4 323 356 10.1002/cpe.938 (Pubitemid 40285781)
40. M. Tomassini, Parallel and distributed evolutionary algorithms: a review, in Evolutionary Algorithms in Engineering and Computer Science-Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications, ed. by K. Miettinen, M.M. Mäkelä, P. Neittaanmäki, J. Périaux (Chichester, UK, 1999), pp. 113-133
41. P. Troger, H. Rajic, A. Haas, P. Domagalski, Standardization of an API for distributed resource management systems, in CCGRID '07: Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (Washington, DC, 2007) pp. 619-626
42. E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization, in EUROGEN 2001, Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (Athens, 2001)