A Multi-cluster Grid Enabled Evolution Framework for Aerodynamic Airfoil Design Optimization

Advances in grid computing have recently sparkled the research and development of Grid problem solving environments for complex design. Parallelism in the form of distributed computing is a growing trend, particularly so in the optimization of high-fidelity computationally expensive design problems...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 1112 - 1121
Main Authors Ng, Hee-Khiang, Lim, Dudy, Ong, Yew-Soon, Lee, Bu-Sung, Freund, Lars, Parvez, Shuja, Sendhoff, Bernhard
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Analysis Code
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Grid Service
Mechanical engineering. Machine design
Parallel Genetic Algorithm
Software
Standard Genetic Algorithm
Wall Clock Time
Fidelity
High performance
Parallel algorithm
Evolutionary algorithm
Grid
Cluster computing
Aerodynamics
Distributed system
Distributed computing
Optimization
Engineering design
Genetic algorithm
Multigrid
Problem solving
Parallelism
Aerofoil
Parallelization
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Summary:Advances in grid computing have recently sparkled the research and development of Grid problem solving environments for complex design. Parallelism in the form of distributed computing is a growing trend, particularly so in the optimization of high-fidelity computationally expensive design problems in science and engineering. In this paper, we present a powerful and inexpensive grid enabled evolution framework for facilitating parallelism in hierarchical parallel evolutionary algorithms. By exploiting the grid evolution framework and a multi-level parallelization strategy of hierarchical parallel GAs, we present the evolutionary optimization of a realistic 2D aerodynamic airfoil structure. Further, we study the utility of hierarchical parallel GAs on two potential grid enabled evolution frameworks and analysis how it fares on a grid environment with multiple heterogeneous clusters, i.e., clusters with differing specifications and processing nodes. From the results, it is possible to conclude that a grid enabled hierarchical parallel evolutionary algorithm is not mere hype but offers a credible alternative, providing significant speed-up to complex engineering design optimization.
ISBN:9783540283256
3540283250
3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539117_151