Hierarchical distributed metamodel‐assisted evolutionary algorithms in shape optimization

In aerodynamic shape optimization, the availability of multiple evaluation models of different precision and hence computational cost can be efficiently exploited in a hierarchical evolutionary algorithm. Thus, in this work the demes of a distributed evolutionary algorithm are ordered in levels, with each level employing a different flow analysis method, giving rise to a hierarchical distributed scheme. The arduous task of exploring the design space is undertaken by demes consisting the lower hierarchy level, which use a low-cost flow analysis tool, namely a viscous–inviscid flow interaction method. Promising solutions are directed towards the higher level, where these are further evolved based on a high precision/cost evaluation tool, viz. a Navier–Stokes equations solver. The final, optimal solution is obtained from the highest hierarchy level. At each level, metamodels, trained on-line on the outcome of evaluations with the level's analysis tool, are used. The role of metamodels is to allow a parsimonious use of computational resources by filtering the poorly performing individuals in each deme. The entire algorithm has been implemented so as to take advantage of a parallel computing system. The efficiency and effectiveness of the proposed hierarchical distributed evolutionary algorithm have been assessed in the design of a transonic isolated airfoil and a compressor cascade. Remarkable superiority over the conventional evolutionary algorithms has been monitored. Copyright © 2006 John Wiley & Sons, Ltd.

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