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Journal of Global Optimization

, Volume 69, Issue 1, pp 117–136 | Cite as

GOSAC: global optimization with surrogate approximation of constraints

  • Juliane MüllerEmail author
  • Joshua D. Woodbury
Article

Abstract

We introduce GOSAC, a global optimization algorithm for problems with computationally expensive black-box constraints and computationally cheap objective functions. The variables may be continuous, integer, or mixed-integer. GOSAC uses a two-phase optimization approach. The first phase aims at finding a feasible point by solving a multi-objective optimization problem in which the constraints are minimized simultaneously. The second phase aims at improving the feasible solution. In both phases, we use cubic radial basis function surrogate models to approximate the computationally expensive constraints. We iteratively select sample points by minimizing the computationally cheap objective function subject to the constraint function approximations. We assess GOSAC’s efficiency on computationally cheap test problems with integer, mixed-integer, and continuous variables and two environmental applications. We compare GOSAC to NOMAD and a genetic algorithm (GA). The results of the numerical experiments show that for a given budget of allowed expensive constraint evaluations, GOSAC finds better feasible solutions more efficiently than NOMAD and GA for most benchmark problems and both applications. GOSAC finds feasible solutions with a higher probability than NOMAD and GOSAC.

Keywords

Black-box optimization Computationally expensive constraints Surrogate models Radial basis functions 

Abbreviations

GOSAC

Global optimization with surrogate approximation of constraints

NOMAD

Nonlinear optimization by mesh adaptive direct search, version 3.6.2

RBF

Radial basis function

List of symbols

\({\mathbf {x}}\)

Decision variable vector (may be continuous, integer, binary, mixed-integer)

\({\mathbf {x}}^T\)

Transpose of \({\mathbf {x}}\)

d

Problem dimension

\(x_i^l\), \(x_i^u\), \(i = 1\ldots d\)

Variables’ lower and upper bounds

\(\varOmega \)

Feasible domain defined by variables’ lower and upper bounds

\(\varOmega _c\)

Feasible domain defined by all constraints

\({{\mathbb {I}}}\)

Index set of integer variables

\(f(\cdot )\)

Objective function, computationally cheap

\(c_j(\cdot ), j = 1\ldots m\)

Computationally expensive black-box constraint functions

\(s_j(\cdot )\), \(j = 1\ldots m\)

Response surface for the jth constraint function

\({\mathcal {S}}\)

Set of already evaluated points, \({\mathcal {S}}=\{{\mathbf {x}}_1,\ldots ,{\mathbf {x}}_n\}\)

\(n_0\)

Number of points in initial experimental design

Mathematics Subject Classification

90C26 90C30 90C56 90C59 

Supplementary material

10898_2017_496_MOESM1_ESM.pdf (227 kb)
Supplementary material 1 (pdf 226 KB)

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Copyright information

© Springer Science+Business Media New York (outside the USA) 2017

Authors and Affiliations

  1. 1.Lawrence Berkeley National Laboratory, Computational Research Division, Center for Computational Sciences and EngineeringBerkeleyUSA
  2. 2.Swiss ReArmonkUSA

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