In science, engineering and economics, decision problems are frequently modelled by
optimizing the value of a (primary) objective function under stated feasibility constraints. In …

In this paper, a global optimization problem is considered where the objective function and
constraints are multiextremal black‐box functions satisfying the Lipschitz condition over a …

In this work, we propose a computationally efficient algorithm for the problem of global
optimization in univariate loss functions. For the performance evaluation, we study the …

In this paper a new class of multidimensional global optimization algorithms (called" divide
the best" algorithms) is proposed. The class unifies and generalizes the classes of the …

In this survey, univariate global optimization problems are considered where the objective
function or its first derivative can be multiextremal black-box costly functions satisfying the …

In this paper we address the biobjective problem of locating a semiobnoxious facility, that
must provide service to a given set of demand points and, at the same time, has some …

We present improvements to branch and bound techniques for globally optimizing functions
with Lipschitz continuity properties by developing novel bounding procedures and …

In this thesis, we present new methods for solving nonlinear optimization problems. These
problems are difficult to solve because the nonlinear constraints form feasible regions that …

In this thesis, we develop constrained simulated annealing (CSA), a global optimization
algorithm that asymptotically converges to constrained global minima (CGM dn) with …

Covering methods constitute a broad class of algorithms for solving multivariate Global
Optimization problems. In this note we show that, when the objective f is dc and a dc …