Asynchronous evolutionary shape optimization based on high-quality surrogates: application to an air-conditioning duct
Multi-processor HPC tools have become commonplace in industry and research today. Evolutionary algorithms may be elegantly parallelized by broadcasting a whole population of designs to an array of processors in a computing cluster or grid. However, issues arise due to synchronization barriers: subsequent iterations have to wait for the successful execution of all jobs of the previous generation. When other users load a cluster or a grid, individual tasks may be delayed and some of them may never complete, slowing down and eventually blocking the optimization process. In this paper, we extend the recent Futures concept permitting the algorithm to circumvent such situations. The idea is to set the default values to the cost function values calculated using a high-quality surrogate model, progressively improving when exact numerical results are received. While waiting for the exact result, the algorithm continues using the approximation and when the data finally arrives, the surrogate model is updated. At convergence, the final result is not only an optimized set of designs, but also a surrogate model that is precise within the neighborhood of the optimal solution. We illustrate this approach with the cluster optimization of an A/C duct of a passenger car, using a refined CFD legacy software model along with an adaptive meta-model based on Proper Orthogonal Decomposition (POD) and diffuse approximation.
futures parallel computing genetic algorithms ask&tell fluid mechanics