Inexact information aided, low-cost, distributed genetic algorithms for aerodynamic shape optimization

Despite its robustness, the design and optimization of aerodynamic shapes using genetic algorithms suffers from high computing cost requirements, due to excessive calls to Computational Fluid Dynamics tools for the evaluation of candidate solutions. To alleviate this problem, either the use of distributed genetic algorithms or the implementation of surrogate evaluation models have separately been proposed in the past. A distributed genetic algorithm relies on the handling of population subsets that evolve in a semi-isolated manner by regularly exchanging their best individuals. It is known that distributed schemes generally outperform single-population ones. On the other hand, the implementation of less costly surrogate evaluation tools, such as the autocatalytic radial basis function networks developed by the authors for the purpose of getting rid of most of the ʹuselessʹ exact evaluations, reduces considerably the computational cost. The aim of the present paper is to employ a surrogate evaluation model in the context of a distributed genetic algorithm and to demonstrate that the combination of both results in maximum economy in CPU cost. In addition, whenever a multiprocessor system is available, the gain is much more pronounced, since the new optimization method maximizes parallel efficiency. The proposed method is used to solve inverse design and optimization problems in aeronautics and turbomachinery.