CONORBIT: Constrained Optimization By Radial Basis Function Interpolation In Trust Regions
Abstract: This paper presents CONORBIT (CONstrained Optimization by Radial Basis function Interpolation in Trust regions), 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 [S.M. Wild, R.G. Regis and C.A. Shoemaker, ORBIT: optimization by radial basis function interpolation in trust-regions, SIAM J. Sci. Comput. 30 (2008), pp.3197-3219]. It uses a small margin for the RBF constraint models 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.
Regis, Rommel G., and Stefan M. Wild. "CONORBIT: Constrained Optimization By Radial Basis Function Interpolation In Trust Regions." Optimization Methods and Software 32.3 (2017): 552-580.
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