Grid enabled, hierarchical distributed metamodel-assisted evolutionary algorithms for aerodynamic shape optimization

Future Generation Computer Systems

Volumn 24, Issue 7, 2008, Pages 701-708

  Liakopoulos, P I K ^a   Kampolis, I C ^a   Giannakoglou, K C ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Distributed search; Evolutionary algorithms; Grid computing; Hierarchical search; Metamodels; Optimization

Indexed keywords

AERODYNAMICS; AUTHENTICATION; CLUSTER ANALYSIS; GRID COMPUTING; OPTIMIZATION; PROBLEM SOLVING; RESOURCE ALLOCATION;

DISTRIBUTED SEARCH; HIERARCHICAL AND DISTRIBUTED SCHEME (HDMAEA); HIERARCHICAL SEARCH; METAMODELS;

EVOLUTIONARY ALGORITHMS;

EID: 44149110828     PISSN: 0167739X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.future.2008.03.004     Document Type: Article

References (37)

1. M. Nowostawski, R. Poli, Parallel genetic algorithm taxonomy. in: Proc. of the Third International Conference on Knowledge-Based Intelligent Information Engineering Systems KES'99, 1999, pp. 88-92
2. Cantu-Paz E. A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systemes Repartis 10 2 (1998) 141-171
3. Alba E., and Tomassini M. Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6 5 (2002)
4. Ochi L.S., Vianna D.S., Drummond L.M.A., and Victor A.O. A parallel evolutionary algorithm for the vehicle routing problem with heterogeneous fleet. Future Generation Computer Systems 14 5-6 (1998) 285-292
5. D.J. Doorly, J. Peiró, S. Spooner, Design optimisation using distributed evolutionary methods, in: 37th Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, January 1999. AIAA-1999-111
6. Giannakoglou K.C., Giotis A.P., and Karakasis M.K. Low-cost genetic optimization based on inexact pre-evaluations and the sensitivity analysis of design parameters. Journal of Inverse Problems in Engineering 9 4 (2001) 389-412
7. Giannakoglou K.C. Design of optimal aerodynamic shapes using stochastic optimization methods and computational intelligence. Progress in Aerospace Sciences 38 1 (2002) 43-76
8. Karakasis M.K., Giotis A.P., and Giannakoglou K.C. Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization. International Journal for Numerical Methods in Fluids 43 10-11 (2003) 1149-1166
9. I.C. Kampolis, K.C. Giannakoglou, A multilevel approach to single- and multiobjective aerodynamic optimization. Computer Methods in Applied Mechanics and Engineering 2008 (in press)
10. Eres M.H., Pound G.E., Jiao Z., Wason J.L., Xu F., Keane A.J., and Cox S.J. Implementation and utilisation of a grid-enabled problem solving environment in matlab. Future Generation Computer Systems 21 6 (2005) 920-929
11. Lim D., Ong Y., Jin Y., Sendhoff B., and Lee B. Efficient hierarchical parallel genetic algorithms using grid computing. Future Generration Computer Systems 23 4 (2007) 658-670
12. Abdalhaq B., Cortés A., Margalef T., and Luque E. Enhancing wildland fire prediction on cluster systems applying evolutionary optimization techniques. Future Generation Computer Systems 21 1 (2005) 61-67
13. I.C. Kampolis, K.C. Giannakoglou, A multilevel approach to single- and multiobjective aerodynamic optimization. Computer Methods in Applied Mechanics and Engineering 2008 (in press)
14. Myers R.H., and Montgomery D.C. Response surface methodology: Process and product optimization using designed experiments. Probability and Statistics. 2nd ed. (2002), John Wiley & Sons, New York, USA
15. T.W. Simpson, T.M. Mauery, J.J. Korte, F. Mistree, Comparison of response surface and kriging models for multidisciplinary design optimization, AIAA Paper 98-4755, 1998
16. Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed. (1999), Prentice Hall, NJ, USA
17. Pierret S., and Van den Braembussche R.A. Turbomachinery blade design using a Navier-Stokes solver and artificial neural network. Journal of Turbomachinery 121 2 (1999) 326-332 ASME Paper 99-GT-4
18. Büche D., Schraudolph N., and Koumoutsakos P. Accelerating evolutionary algorithms with gaussian process fitness function models. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 35 2 (2005) 183-194
19. Montgomery D.C. Design and Analysis of Experiments. 6th ed. (2005), John Wiley & Sons, NY, USA
20. A. Ratle, Optimal sampling strategies for learning a fitness model, in: Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3, Washington, DC, July 1999, pp. 2078-2085
21. Jin Y., Olhofer M., and Sendhoff B. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation 6 5 (2002) 481-494
22. Y.S. Ong, K.Y. Lum, P.B. Nair, D.M. Shi, Z.K. Zhang, Global convergence of unconstrained and bound constrained surrogate-assisted evolutionary search in aerodynamic shape design, in: Proceedings of the 2003 Congress on Evolutionary Computation, CEC'03, vol. 3, Canberra, Australia, December 2003, pp. 1856-1863
23. H. Ulmer, F. Streichert, A. Zell, Evolution strategies assisted by gaussian processes with improved pre-selection criterion, in: Proceedings of the 2003 Congress on Evolutionary Computation, CEC '03, vol. 1, Canberra, Australia, 2003, pp. 692-699
24. A.P. Giotis, K.C. Giannakoglou, J. Périaux, A reduced-cost multi-objective optimization method based on the Pareto front technique, neural networks and PVM, in: E. Oñate, G. Bugeda, B. Suárez (Eds.) ECCOMAS 2000, Proceedings of the European Congress on Computational Methods in Applied Sciences and Engineering, CIMNE, Barcelona, Spain, September 2000
25. Karakasis M.K., and Giannakoglou K.C. On the use of metamodel-assisted, multi-objective evolutionary algorithms. Engineering Optimization 38 8 (2006) 941-957
26. E. Zitzler, M. Laumans, L. Thiele, SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Swiss Federal Institute of Technology (ETH), Computer Engineering and Communication Networks Lab. (TIK), Zurich, Switzerland, May 2001
27. Tomassini M. Parallel and distributed evolutionary algorithms: A review. In: Miettinen K., Mäkelä M.M., Neittaanmäki P., and Périaux J. (Eds). Evolutionary Algorithms in Engineering and Computer Science - Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications (1999), John Wiley & Sons, Chichester, UK 113-133
28. Alba E., and Tomassini M. Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation 6 5 (2002) 443-462
29. Tanese R. Distributed genetic algorithms. In: Schaffer J.D. (Ed). Proceedings of the 3rd International Conference on Genetic Algorithms (1989), Morgan Kaufmann Publishers Inc., San Francisco, CA, USA 434-439
30. Alba E., and Troya J.M. Influence of the migration policy in parallel distributed GAs with structured and panmictic populations. Applied Intelligence 12 3 (2000) 163-181
31. Herrera F., and Lozano M. Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation 4 1 (2000) 43-63
32. Thain D., Tannenbaum T., and Livny M. Distributed computing in practice: The Condor experience. Concurrency - Practice and Experience 17 2-4 (2005) 323-356
33. Foster I. Globus toolkit version 4: Software for service-oriented systems. LNCS (2006) 2-13
34. L.M. Matthew, N.C. Brent, E.C. David, The ganglia distributed monitoring system: Design, implementation and experience, Technical Report, University of California, Berkeley, February 2003
35. Montero R.S., Huedo E., and Llorente I.M. A framework for adaptive execution on grids. Journal of Software-Practice and Experience 34 (2004) 631-651
36. Drela M., and Giles M.B. Viscous-inviscid analysis of transonic and low reynolds number airfoils. AIAA Journal 25 10 (1987) 1347-1355
37. Lambropoulos N.K., Koubogiannis D.G., and Giannakoglou K.C. Acceleration of a Navier-Stokes equation solver for unstructured grids using agglomeration multigrid and parallel processing. Computer Methods in Applied Mechanics and Engineering 193 (2004) 781-803