Infill Criterion for Multimodal Model-Based Optimisation

TL;DR

This paper introduces a new infill criterion for model-based optimization that effectively identifies multiple local optima, enhancing the generation of diverse, challenging test scenarios for physical systems.

Contribution

The paper proposes a novel infill criterion tailored for multimodal optimization, improving local optima detection over existing methods in simulation-based testing.

Findings

The new criterion outperforms expected improvement in local optima identification.

It demonstrates superior performance compared to Latin Hypercube Sampling.

Validated on fifteen artificial functions.

Abstract

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.