Direct-type global optimization algorithms often spend an excessive number of function
evaluations on problems with many local optima exploring suboptimal local minima, thereby …

This article considers a box-constrained global optimization problem for Lipschitz
continuous functions with an unknown Lipschitz constant. The well-known derivative-free …

In this work, we introduce DIRECTGO, a new MATLAB toolbox for derivative-free global
optimization. DIRECTGO collects various deterministic derivative-free DIRECT-type …

This paper proposes a novel infill sampling criterion for constraint handling in multi-objective
optimization of computationally expensive black-box functions. To reduce the computational …

We consider a box-constrained global optimization problem with a Lipschitz-continuous
objective function and an unknown Lipschitz constant. The well known derivative-free global …

We consider a global optimization problem for Lipschitz-continuous functions with an
unknown Lipschitz constant. Our approach is based on the well-known DIRECT (DIviding …

Although DIRECT global optimization algorithm quickly gets close to the basin of the
optimum, it often takes much longer to refine the solution to a high degree of accuracy. This …

In this paper we consider bound constrained global optimization problems where first-order
derivatives of the objective function can be neither computed nor approximated explicitly …

Applied optimization problems often include constraints. Although the well-known derivative-
free global-search DIRECT algorithm performs well solving box-constrained global …

Recent metamodel-based global optimization algorithms are very promising for box-
constrained expensive optimization problems. However, few of them can tackle constrained …