Towards Better Integration of Surrogate Models and Optimizers

Surrogate-Assisted Evolutionary Algorithms (SAEAs) have been proven to be very effective in solving (synthetic and real-world) computationally expensive optimization problems with a limited number of function evaluations. The two main components of SAEAs are: the surrogate model and the evolutionary optimizer, both of which use parameters to control their respective behavior. These parameters are likely to interact closely, and hence the exploitation of any such relationships may lead to the design of an enhanced SAEA. In this chapter, as a first step, we focus on Kriging and the Efficient Global Optimization (EGO) framework. We discuss potentially profitable ways of a better integration of model and optimizer. Furthermore, we investigate in depth how different parameters of the model and the optimizer impact optimization results. In particular, we determine whether there are any interactions between these parameters, and how the problem characteristics impact optimization results. In the experimental study, we use the popular Black-Box Optimization Benchmarking (BBOB) testbed. Interestingly, the analysis finds no evidence for significant interactions between model and optimizer parameters, but independently their performance has a significant interaction with the objective function. Based on our results, we make recommendations on how best to configure EGO

Automated Response Surface Model Generation with Sequential Design

The increasing use of expensive computer simulations in engineering places a serious computational burden on associated optimization problems. Surrogate modelling becomes standard practice in analyzing such expensive blackbox problems. Moreover, surrogate based optimization (SBO) is able to drastically reduce the number of needed function evaluations with respect to traditional methods. This paper briefly discusses several approaches available which use surrogate models for optimization and highlights one sequential design approach in particular, i.e., expected improvement. Expected improvement is described in detail, along with recent related work. The approach has been implemented in a readily available research platform for surrogate modelling and demonstrated on a concrete application from Electro-Magnetics (EM). The results hold competitive designs and one optimum is even able to outperform the reference optimum obtained using extensive domain specific knowledge

Multi-objective optimization of reflector antennas using kriging and probability of improvement

This paper presents the multi-objective optimization design of a blocked prime focus reflector system using a Kriging response surface approximation technique with adaptive sampling. Samples are added to the model by selecting those with the highest probability of improving the current estimate of the Pareto front. The problem is especially difficult due to the multi-modal nature of the Pareto front, and a good estimate is achieved using only a modest number of full wave simulations