A Bayesian approach to constrained single- and multi-objective optimization

TL;DR

This paper introduces BMOO, a Bayesian optimization method for efficiently solving complex, expensive, constrained single- and multi-objective problems by using an extended domination rule and a novel hyper-volume improvement criterion.

Contribution

It proposes a unified Bayesian framework with a new sampling criterion and Monte Carlo techniques for constrained multi-objective optimization, addressing feasibility and efficiency.

Findings

BMOO outperforms existing algorithms in complex constrained problems.

The method effectively finds feasible solutions with limited evaluations.

The approach adapts to both feasible and infeasible search scenarios.

Abstract

This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can be used to carry out the optimization is very limited, as in complex industrial design optimization problems. The method we propose to overcome this difficulty has its roots in both the Bayesian and the multi-objective optimization literatures. More specifically, an extended domination rule is used to handle objectives and constraints in a unified way, and a corresponding expected hyper-volume improvement sampling criterion is proposed. This new criterion is naturally adapted to the search of a feasible point when none is available, and reduces to existing Bayesian sampling criteria---the classical Expected Improvement (EI) criterion and some of its constrained/multi-objective extensions---as soon as at least one feasible point is available. The calculation and optimization of the criterion are performed using Sequential Monte Carlo techniques. In particular, an algorithm similar to the subset simulation method, which is well known in the field of structural reliability, is used to estimate the criterion. The method, which we call BMOO (for Bayesian Multi-Objective Optimization), is compared to state-of-the-art algorithms for single- and multi-objective constrained optimization.