Constraint handling in efficient global optimization

TL;DR: A new EGO-based algorithm is introduced which tries to overcome common issues with Kriging optimization algorithms, mainly: early stagnation, problems with multiple active constraints and frequent crashes.

Abstract: Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm It was originally proposed to address unconstrained problems and later was modified to solve constrained problems However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes In this work, we introduce a new EGO-based algorithm which tries to overcome these common issues with Kriging optimization algorithms We apply the proposed algorithm on problems with dimension d ≤ 4 from the G-function suite [16] and on an airfoil shape example