Surrogate-based optimization for overflow spillway design

Surrogate based Evolutionary Algorithm for Design Optimization

Optimization is often a critical issue for most system design problems. Evolutionary Algorithms are population-based, stochastic search techniques, widely used as efficient global optimizers. However, finding optimal solution to complex high dimensional, multimodal problems often require highly computationally expensive function evaluations and hence are practically prohibitive. The Dynamic App...

17 Surrogate - Based Optimization

Surrogate-Based Optimization

Surrogate-based optimization (Queipo et al. 2005, Simpson et al. 2008) represents a class of optimization methodologies that make use of surrogate modeling techniques to quickly find the local or global optima. It provides us a novel optimization framework in which the conventional optimization algorithms, e.g. gradient-based or evolutionary algorithms are used for sub-optimization(s). Surrogat...

RBF-based surrogate model for evolutionary optimization

Many today’s engineering tasks use approximation of their expensive objective function. Surrogate models, which are frequently used for this purpose, can save significant costs by substituting some of the experimental evaluations or simulations needed to achieve an optimal or near-optimal solution. This paper presents a surrogate model based on RBF networks. In contrast to the most of the surro...

Learning surrogate models for simulation-based optimization

We address a central problem in modeling, namely that of learning an algebraic model from data obtained from simulations or experiments. We propose a methodology that uses a small number of simulations or experiments to learn models that are as accurate and as simple as possible. The approach begins by building a low-complexity surrogate model. The model is built using a best subset technique t...