Hierarchically constrained blackbox optimization

TL;DR

This paper introduces hierarchical strategies for blackbox optimization that efficiently interrupt evaluations when constraints indicate no potential improvement, reducing computational costs.

Contribution

It proposes novel hierarchical constraint evaluation methods and compares their effectiveness in blackbox optimization scenarios.

Findings

Hierarchical strategies reduce evaluation time.

Interrupting evaluations saves computational resources.

Effective in test problem scenarios.

Abstract

In blackbox optimization, evaluation of the objective and constraint functions is time consuming. In some situations, constraint values may be evaluated independently or sequentially. The present work proposes and compares two strategies to define a hierarchical ordering of the constraints and to interrupt the evaluation process at a trial point when it is detected that it will not improve the current best solution. Numerical experiments are performed on a closed-form test problem.