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Most genetic algorithms (GAs) used in literature to solve control problems are time consuming and involve important storage memory requirements. In fact, the search in GAs is iteratively performed on a population of chromosomes (control parameters). As a result, the cost functional needs to be evaluated through solving the high fidelity model or by performing the experimental protocol for each chromosome and for many generations. To overcome this issue, a non intrusive reduced real coded genetic algorithm (RGA) for near real time optimal control is designed. This algorithm uses precalculated parametrized solution snapshots stored in the POD (Proper Orthogonal Decomposition) reduced form, to predict the solution snapshots for chromosomes over generations. The method used for this purpose is a hyper reduced version of the Bi-CITSGM method (Bi Calibrated Interpolation on the Tangent Space of the Grassmann Manifold) designed specially for non linear parametrized solution snapshots interpolation. This hyper reduced approach referred to as Hyper Bi-CITSGM, is proposed in such a way to accelerate the usual Bi-CITSGM process by bringing this last to a significantly low dimension. Thus, the whole optimization process by RGA can be performed in near real time. The potential of RGA in terms of accuracy and CPU time is demonstrated on control problems of the flow past a cylinder and flow in a lid driven cavity when the Reynolds number value varies. |
