Abstract
Abstract: An overview of modern optimization methods, including Evolutionary Al-gorithms (EAs) and gradient{based optimization methods adapted for Cluster and Grid Computing is presented. The basic tool is a Hierarchical Distributed Metamodel{Assisted EA supporting Multilevel Evaluation, Multilevel Search and Multilevel Parameterization. In this framework, the adjoint method computes the first and second derivatives of the objective function with respect to the design variables, for use in aerodynamic shape optimization. Such a multi{component, hierarchical and distributed scheme requires particular attention when Cluster or Grid Computing is used and a much more delicate parallelization compared to that of conventional EAs.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
D. Lim, Y-S. Ong, Y. Jin, B. Sendhoff, B-S. Lee, Efficient Hierarchical Parallel Genetic Algorithms using Grid Computing, J. Fut. Gener. Comput. Syst., 23(4):658–670, 2007.
N. Lambropoulos, D. Koubogiannis, K. Giannakoglou, Acceleration of a Navier-Stokes Equation Solver for Unstructured Grids using Agglomeration Multigrid and Parallel Processing, Comp. Meth. Appl. Mech. Eng., 193:781–803, 2004.
A. Giotis, K. Giannakoglou, An Unstructured Grid Partitioning Method Based on Genetic Algorithms, Advances in Engineering Software, 29(2):129–138, 1998.
G. Karypis, V. Kumar, Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs, SIAM J. Scientific Computing, 20(1):359–392, 1998.
K. Giannakoglou, Design of Optimal Aerodynamic Shapes using Stochastic Optimization Methods and Computational Intelligence,Int. Review J. Progress in Aerospace Sciences, 38:43–76, 2002.
M. Karakasis, D. Koubogiannis, K. Giannakoglou, Hierarchical Distributed Evolutionary Algorithms in Shape Optimization, Int. J. Num. Meth. Fluids, 53:455–469, 2007.
D. Papadimitriou, K. Giannakoglou, A Continuous Adjoint Method with Objective Function Derivatives Based on Boundary Integrals for Inviscid and Viscous Flows, Computers & Fluids, 36:325–341, 2007.
D. Papadimitriou, K. Giannakoglou, Direct, Adjoint and Mixed Approaches for the Computation of Hessian in Airfoil Design Problems, Int. J. Num. Meth. Fluids, to appear.
E. Cantu-Paz, A Survey of Parallel Genetic Algorithms”, Calculateurs Paralleles, Reseaux et Systemes Repartis, 10(2):141–171, 1998.
M. Nowostawski, R. Poli, Parallel Genetic Algorithm Taxonomy, Proc. 3rd Int. Conf. on Knowledge-based Intelligent Information Engineering Systems KES’99:88–92, IEEE, 1999.
E. Alba, M. Tomassini, Parallelism and Evolutionary Algorithms, IEEE Trans. Evol. Comp., 6(5), Oct. 2002.
A. Keane, P. Nair, Computational Approaches for Aerospace Design. The Pursuit of Excellence, John Wiley & Sons, Ltd, 2005.
K. Giannakoglou, A. Giotis, M. Karakasis, Low–Cost Genetic Optimization based on Inexact Pre-evaluations and the Sensitivity Analysis of Design Parameters, Inverse Problems in Engineering, 9:389–412, 2001.
M. Karakasis, K. Giannakoglou, Inexact Information aided, Low-Cost, Distributed Genetic Algorithms for Aerodynamic Shape Optimization, Int. J. Num. Meth. Fluids, 43(10–11):1149–1166, 2003.
N. Srinivas, K. Deb, Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms, Evolutionary Computation, 2(3):221–248, 1995.
E. Zitzler, M. Laumans, L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Report, Swiss Federal Institute of Technology (ETH), Computer Engineering and Communication Networks Lab., May 2001.
D. Papadimitriou, K. Giannakoglou, Total Pressure Losses Minimization in Turbomachinery Cascades, Using a New Continuous Adjoint Formulation, Journal of Power and Energy (Special Issue on Turbomachinery), to appear, 2007.
K. Giannakoglou, D. Papadimitriou, Adjoint Methods for gradient- and Hessian-based Aerodynamic Shape Optimization, EUROGEN 2007, Jyvaskyla, June 11–13, 2007.
D. Thain and T. Tannenbaum, M. Livny, Distributed computing in practice: the Condor experience, Concurrency - Practice and Experience, 17(2–4):323–356, 2005.
I. Foster, Globus Toolkit Version 4: Software for Service-Oriented Systems, International Conference on Network and Parallel Computing, LNCS: 2–1317(2–4), Springer-Verlag, 2006.
E. Huedo, R. Montero, I. Llorente, A Framework for Adaptive Execution on Grids, Journal of Software - Practice and Experience, 34:631–651, 2004.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giannakoglou, K. et al. (2009). Aerodynamic Shape Optimization Methods on Multiprocessor Platforms. In: Parallel Computational Fluid Dynamics 2007. Lecture Notes in Computational Science and Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92744-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-92744-0_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92743-3
Online ISBN: 978-3-540-92744-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)