Hybrid evolutionary algorithm with Hermite radial basis function interpolants for computationally expensive adjoint solvers |
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Authors: | Y S Ong K Y Lum P B Nair |
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Institution: | (1) School of Computer Engineering, Block N4, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore;(2) Temasek Laboratories, National University of Singapore, 5 Sports Drive 2, Singapore, 117508, Singapore;(3) Computational Engineering and Design Group, School of Engineering Sciences, University of Southampton, Highfield, Southampton, SO17 1BJ, England |
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Abstract: | In this paper, we present an evolutionary algorithm hybridized with a gradient-based optimization technique in the spirit
of Lamarckian learning for efficient design optimization. In order to expedite gradient search, we employ local surrogate
models that approximate the outputs of a computationally expensive Euler solver. Our focus is on the case when an adjoint
Euler solver is available for efficiently computing the sensitivities of the outputs with respect to the design variables.
We propose the idea of using Hermite interpolation to construct gradient-enhanced radial basis function networks that incorporate
sensitivity data provided by the adjoint Euler solver. Further, we conduct local search using a trust-region framework that
interleaves gradient-enhanced surrogate models with the computationally expensive adjoint Euler solver. This ensures that
the present hybrid evolutionary algorithm inherits the convergence properties of the classical trust-region approach. We present
numerical results for airfoil aerodynamic design optimization problems to show that the proposed algorithm converges to good
designs on a limited computational budget. |
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Keywords: | Hybrid evolutionary algorithm Hermite radial basis function Gradient-based approximation Computationally expensive adjoint solver |
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