Global optimization advances in mixed-integer nonlinear programming, MINLP, and constrained derivative-free optimization, CDFO
This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …
A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods
Computationally expensive multiobjective optimization problems arise, eg in many
engineering applications, where several conflicting objectives are to be optimized …
engineering applications, where several conflicting objectives are to be optimized …
RBFOpt: an open-source library for black-box optimization with costly function evaluations
We consider the problem of optimizing an unknown function given as an oracle over a mixed-
integer box-constrained set. We assume that the oracle is expensive to evaluate, so that …
integer box-constrained set. We assume that the oracle is expensive to evaluate, so that …
Electric bus charging facility planning with uncertainties: Model formulation and algorithm design
This paper investigates the electric bus charging facility planning (EB-CFP) problem for a
bus transit company operating a heterogeneous electric bus (EB) fleet to provide public …
bus transit company operating a heterogeneous electric bus (EB) fleet to provide public …
Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization
This article presents the DYCORS (DYnamic COordinate search using Response Surface
models) framework for surrogate-based optimization of HEB (High-dimensional, Expensive …
models) framework for surrogate-based optimization of HEB (High-dimensional, Expensive …
Surrogate‐based methods for black‐box optimization
In this paper, we survey methods that are currently used in black‐box optimization, that is,
the kind of problems whose objective functions are very expensive to evaluate and no …
the kind of problems whose objective functions are very expensive to evaluate and no …
[BOOK][B] Global optimization: theory, algorithms, and applications
M Locatelli, F Schoen - 2013 - SIAM
The first systematic overviews on global optimization appeared in 1975–1978 thanks to two
fundamental volumes titled Towards Global Optimization (Dixon & Szegö, 1975, 1978). At …
fundamental volumes titled Towards Global Optimization (Dixon & Szegö, 1975, 1978). At …
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques
Optimization of simulation-based or data-driven systems is a challenging task, which has
attracted significant attention in the recent literature. A very efficient approach for optimizing …
attracted significant attention in the recent literature. A very efficient approach for optimizing …
Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points
RG Regis - Engineering Optimization, 2014 - Taylor & Francis
This article develops two new algorithms for constrained expensive black-box optimization
that use radial basis function surrogates for the objective and constraint functions. These …
that use radial basis function surrogates for the objective and constraint functions. These …
ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems
The algorithmic framework ARGONAUT is presented for the global optimization of general
constrained grey-box problems. ARGONAUT incorporates variable selection, bounds …
constrained grey-box problems. ARGONAUT incorporates variable selection, bounds …