Consensus-based Optimization and Ensemble Kalman Inversion for Global Optimization Problems with Constraints

TL;DR

This paper presents a novel approach to constrained global optimization using stochastic particle systems, combining penalization and relaxation techniques to effectively handle constraints without restricting particle dynamics.

Contribution

The paper introduces a new method that incorporates constraints into CBO and EKI without restricting particles to feasible regions, using penalization and relaxation strategies.

Findings

Effective handling of constraints via penalization and relaxation.

Theoretical analysis through mean-field Fokker-Planck equations.

Numerical experiments demonstrate promising performance.

Abstract

We introduce a practical method for incorporating equality and inequality constraints in global optimization methods based on stochastic interacting particle systems, specifically consensus-based optimization (CBO) and ensemble Kalman inversion (EKI). Unlike other approaches in the literature, the method we propose does not constrain the dynamics to the feasible region of the state space at all times; the particles evolve in the full space, but are attracted towards the feasible set by means of a penalization term added to the objective function and, in the case of CBO, an additional relaxation drift. We study the properties of the method through the associated mean-field Fokker--Planck equation and demonstrate its performance in numerical experiments on several test problems.