Voronoi-Based Archive Sampling for Robust Optimisation

Abstract

We propose a framework for estimating the quality of solutions in a robust optimisation setting by utilising samples from the search history and using MC sampling to approximate a Voronoi tessellation. This is used to determine a new point in the disturbance neighbourhood of a given solution such that – along with the relevant archived points – they form a well-spread distribution, and is also used to weight the archive points to mitigate any selection bias in the neighbourhood history. Our method performs comparably well with existing frameworks when implemented inside a CMA-ES on 9 test problems collected from the literature in 2 and 10 dimensions.