Engineering design applications of surrogate-assisted optimization techniques

The construction of models aimed at learning the behaviour of a system whose responses to inputs are expensive to measure is a branch of statistical science that has been around for a very long time. Geostatistics has pioneered a drive over the last half century towards a better understanding of the accuracy of such ‘surrogate’ models of the expensive function. Of particular interest to us here are some of the even more recent advances related to exploiting such formulations in an optimization context. While the classic goal of the modelling process has been to achieve a uniform prediction accuracy across the domain, an economical optimization process may aim to bias the distribution of the learning budget towards promising basins of attraction. This can only happen, of course, at the expense of the global exploration of the space and thus finding the best balance may be viewed as an optimization problem in itself. We examine here a selection of the state of-the-art solutions to this type of balancing exercise through the prism of several simple, illustrative problems, followed by two ‘real world’ applications: the design of a regional airliner wing and the multi-objective search for a low environmental impact hous

ACCELERATION OF BUILDING DESIGN OPTIMISATION THROUGH THE USE OF KRIGING SURROGATE MODELS

ABSTRACT This paper describes an experiment to test the performance of Kriging surrogate modelling optimisation techniques on a building design problem with discrete design choices. Surrogate modelling optimisation offers advantages over traditional optimisation techniques on design problems with expensive (time consuming) performance evaluation models. The techniques are tested for both single and multi-objective optimisation problems with the objective of minimising both annual CO 2 emissions predicted by a dynamic simulation and construction cost. The estimated CO 2 emissions and costs of all possible designs were first established through comprehensive analysis using a multi-processor computer, enabling the performance of the optimisation to be assessed precisely against a known single optimum or Pareto front. The performance is compared against an evolutionary algorithm (EA) searching the dynamic simulation model on the same design problem. The results show that for this design problem, Kriging surrogate modelling optimisation is effective at finding estimates of optimum designs. In the case of the single-objective optimisation it is able to find the optimum in fewer simulation calls than the stand-alone EA. In the case of the multi-objective optimisation it is capable of finding a better Pareto front if the total number of simulations is restricted, although the time cost associated with Kriging does not always mean it is worth using

Mining Markov Network Surrogates for Value-Added Optimisation

Surrogate fitness functions are a popular technique for speeding up metaheuristics, replacing calls to a costly fitness function with calls to a cheap model. However, surrogates also represent an explicit model of the fitness function, which can be exploited beyond approximating the fitness of solutions. This paper proposes that mining surrogate fitness models can yield useful additional information on the problem to the decision maker, adding value to the optimisation process. An existing fitness model based on Markov networks is presented and applied to the optimisation of glazing on a building facade. Analysis of the model reveals how its parameters point towards the global optima of the problem after only part of the optimisation run, and reveals useful properties like the relative sensitivities of the problem variables