Constrained Black Box Optimization with Data Analysis

On the Convergence of Adaptive Stochastic Search Methods for Constrained and Multi-objective Black-Box Optimization

Stochastic search methods for global optimization and multi-objective optimization are widely used in practice, especially on problems with black-box objective and constraint functions. Although there are many theoretical results on the convergence of stochastic search methods, relatively few deal with black-box constraints and multiple black-box objectives and previous convergence analyses req...

Constrained Optimization by Radial Basis Function Interpolation for High-Dimensional Expensive Black-Box Problems with Infeasible Initial Points

Use of quadratic models with mesh-adaptive direct search for constrained black box optimization

We consider derivative-free optimization, and in particular black box optimization, where the functions to minimize and the functions representing the constraints are given by black boxes without derivatives. Two fundamental families of methods are available: Model-based methods and directional direct search algorithms. This work exploits the flexibility of the second type of method in order to...

A Derivative-Free Approach to Constrained Multiobjective Nonsmooth Optimization

Abstract. In this work, we consider multiobjective optimization problems with both bound constraints on the variables and general nonlinear constraints, where objective and constraint function values can only be obtained by querying a black box. We define a linesearch-based solution method, and we show that it converges to a set of Pareto stationary points. To this aim, we carry out a theoretic...

CONORBIT: constrained optimization by radial basis function interpolation in trust regions

This paper presents CONORBIT, a derivative-free algorithm for constrained black-box optimization where the objective and constraint functions are computationally expensive. CONORBIT employs a trust-region framework that uses interpolating radial basis function (RBF) models for the objective and constraint functions and is an extension of the ORBIT algorithm (Wild, Regis, and Shoemaker 2008). It...