Guiding surrogate-assisted multi-objective optimisation with decision maker preferences

This is the authors' abstract. We don't add key points for this paper.

We present a new algorithm for efficiently solving expensive multi-and many-objective, black-box optimisation problems by interactively incorporating the preferences of an external decision maker. We define a novel acquisition function which combines the maximin distance to the summary attainment front with a set of aspirational objective-space target points chosen by the decision maker. This drives an exploitative multi-surrogate model to quickly converge to solutions favourable to the decision maker, without relying on surrogate posterior uncertainty estimates or arbitrary objective weighting.