CONORBIT: Constrained Optimization by Radial Basis Function Interpolation in Trust Regions
|Title||CONORBIT: Constrained Optimization by Radial Basis Function Interpolation in Trust Regions|
|Year of Publication||2015|
|Authors||Regis, RG, Wild, SM|
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 uses a small margin for the RBF model constraints to facilitate the generation of feasible iterates, and extensive numerical tests confirm that such a margin is helpful in improving performance. CONORBIT is compared with other algorithms on 27 test problems, a chemical process optimization problem, and an automotive application. Numerical results show that CONORBIT performs better than COBYLA (Powell 1994), a sequential penalty derivative-free method, an augmented Lagrangian method, a direct search method, and another RBF-based algorithm on the test problems and on the automotive application.