Constraint handling in efficient global optimization
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.
Authors: Samineh Bagheri, Wolfgang Konen, Richard Allmendinger, Jürgen Branke, Kalyanmoy Deb, Jonathan Edward Fieldsend, Domenico Quagliarella, Karthik Sindhya
Published in: Genetic and Evolutionary Computation Conference (GECCO) (2017)