Sóbester, András and Keane, Andy J.
Empirical comparisons of gradient-based methods on an engine-inlet shape optimization problem
In Proceedings of the 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization.
American Institute of Aeronautics and Astronautics. 11 pp.
Full text not available from this repository.
With the development of increasingly sophisticated
adjoint flow-solvers capable of providing objective
function gradients at reasonable computational costs,
modern deterministic gradient-based search methods
have come to be regarded as amongst the most
powerful tools in aerodynamic shape optimization and
MDO problems. However, their performance can be
disappointing when the objective function landscape
features multiple local optima, long valleys, noise or
discontinuities. Equally, stochastic global explorers,
such as Genetic Algorithms (GAs), while less affected
by these problems, are relatively slow to converge. In
this paper we propose GLOSSY (Global/Local Search
Strategy), a generic hybrid approach, which combines a
global exploration method with gradient-based
exploitation. We analyze the performance of two
optimizers based on the GLOSSY framework (fusing a
GA with a quasi-Newton local search method) and we
show through a set of comparative tests that on the
moderately noisy objective landscape of a jet-engine
inlet shape optimization problem the hybrid
outperforms both of its components used individually.
We also look at the issue of what global / local search
effort ratio gives the hybrid the best performance.
Actions (login required)