R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation

TL;DR

This paper introduces R-MBO, a multi-surrogate Bayesian optimization method that incorporates decision-maker preferences by modeling each objective separately, overcoming limitations of scalarizing functions and Gaussian assumptions in traditional approaches.

Contribution

The paper proposes a novel multi-surrogate approach for preference incorporation in multi-objective Bayesian optimization, using Generalised value distribution to model non-Gaussian scalarizing functions.

Findings

Outperforms mono-surrogate methods on benchmarks

Effectively incorporates decision-maker preferences

Shows promise on real-world problems

Abstract

Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time to find an approximated set of Pareto optimal solutions. In many real-world problems, a decision-maker has some preferences on the objective functions. One approach to incorporate the preferences in multi-objective BO is to use a scalarising function and build a single surrogate model (mono-surrogate approach) on it. This approach has two major limitations. Firstly, the fitness landscape of the scalarising function and the objective functions may not be similar. Secondly, the approach assumes that the scalarising function distribution is Gaussian, and thus a closed-form expression of an acquisition function e.g., expected improvement can be used. We overcome these limitations by building independent surrogate models (multi-surrogate approach) on each objective function and show that the distribution of the scalarising function is not Gaussian. We approximate the distribution using Generalised value distribution. We present an a-priori multi-surrogate approach to incorporate the desirable objective function values (or reference point) as the preferences of a decision-maker in multi-objective BO. The results and comparison with the existing mono-surrogate approach on benchmark and real-world optimisation problems show the potential of the proposed approach.