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 …
Derivative-free optimization methods
In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …
applications, objective and constraint functions are available only as the output of a black …
Mixed-integer nonlinear optimization
Many optimal decision problems in scientific, engineering, and public sector applications
involve both discrete decisions and nonlinear system dynamics that affect the quality of the …
involve both discrete decisions and nonlinear system dynamics that affect the quality of the …
Review and comparison of algorithms and software for mixed-integer derivative-free optimization
This paper reviews the literature on algorithms for solving bound-constrained mixed-integer
derivative-free optimization problems and presents a systematic comparison of available …
derivative-free optimization problems and presents a systematic comparison of available …
SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems
This paper introduces a surrogate model based algorithm for computationally expensive
mixed-integer black-box global optimization problems with both binary and non-binary …
mixed-integer black-box global optimization problems with both binary and non-binary …
Surrogate-based optimization for mixed-integer nonlinear problems
Simulation-based optimization using surrogate models enables decision-making through
the exchange of data from high-fidelity models and development of approximations. Many …
the exchange of data from high-fidelity models and development of approximations. Many …
The mesh adaptive direct search algorithm for granular and discrete variables
The mesh adaptive direct search (Mads) algorithm is designed for blackbox optimization
problems for which the functions defining the objective and the constraints are typically the …
problems for which the functions defining the objective and the constraints are typically the …
A linesearch-based derivative-free approach for nonsmooth constrained optimization
In this paper, we propose new linesearch-based methods for nonsmooth constrained
optimization problems when first-order information on the problem functions is not available …
optimization problems when first-order information on the problem functions is not available …
Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel
This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a
strategy often adopted to reduce ED overcrowding. We focus on optimizing resource …
strategy often adopted to reduce ED overcrowding. We focus on optimizing resource …
BFO, a trainable derivative-free brute force optimizer for nonlinear bound-constrained optimization and equilibrium computations with continuous and discrete …
A direct-search derivative-free Matlab optimizer for bound-constrained problems is
described, whose remarkable features are its ability to handle a mix of continuous and …
described, whose remarkable features are its ability to handle a mix of continuous and …