Infill Criterion for Multimodal Model-Based Optimisation
This work addresses the need for generating diverse and challenging test scenarios in simulation-based investigations of physical systems, representing an incremental improvement in optimization methods for this domain.
The paper tackles the problem of identifying all local optima in expensive-to-evaluate functions, which correspond to challenging test scenarios for physical systems, by deriving a new infill criterion for model-based optimization that outperforms expected improvement and Latin Hypercube Samples in computer experiments on fifteen artificial functions.
Physical systems are modelled and investigated within simulation software in an increasing range of applications. In reality an investigation of the system is often performed by empirical test scenarios which are related to typical situations. Our aim is to derive a method which generates diverse test scenarios each representing a challenging situation for the corresponding physical system. From a mathematical point of view challenging test scenarios correspond to local optima. Hence, we focus to identify all local optima within mathematical functions. Due to the fact that simulation runs are usually expensive we use the model-based optimisation approach with its well-known representative efficient global optimisation. We derive an infill criterion which focuses on the identification of local optima. The criterion is checked via fifteen different artificial functions in a computer experiment. Our new infill criterion performs better in identifying local optima compared to the expected improvement infill criterion and Latin Hypercube Samples.