surrogatebasierte

Surrogatebasierte methods, often referred to as surrogate-based optimization or response surface methodology, are a class of optimization techniques used when the objective function is computationally expensive to evaluate. Instead of directly optimizing the original, complex function, these methods build a cheaper, approximate model, known as a surrogate model, of the objective function. This surrogate model is then used for optimization, significantly reducing the number of expensive function evaluations required.

The process typically involves an iterative approach. Initially, a small number of points are sampled from the design space and the original objective function is evaluated at these points. A surrogate model, such as a polynomial, radial basis function, or Kriging model, is then constructed based on these sampled data points. This surrogate model is used to identify promising new points to evaluate with the expensive original function. The new function evaluations are then used to refine and improve the accuracy of the surrogate model. This cycle of surrogate modeling, optimization, and expensive function evaluation continues until a satisfactory solution is found or a predefined stopping criterion is met.

Surrogate-based methods are particularly valuable in fields such as engineering design, where simulations or physical experiments can take a substantial amount of time and resources. They are employed to find optimal parameters for complex systems, reduce design iterations, and explore large design spaces efficiently. The choice of surrogate model and the sampling strategy are crucial for the success of these methods, influencing both the accuracy of the approximation and the overall efficiency of the optimization process.