One of the most commonly encountered approaches for the solution of unconstrained global
optimization problems is the application of multi-start algorithms. These algorithms usually …

This paper presents a general approach that combines global search strategies with local
search and attempts to find a global minimum of a real valued function of n variables. It …

A detailed review of the dynamic search trajectory methods for global optimization is given.
In addition, a family of dynamic search trajectories methods that are created using numerical …

Several evolutionary approaches have been applied to global optimization problems with
significant success. Evolution strategies proved to be efficient global optimizers. However …

Global optimization problems involve essential difficulties as, for instance, avoiding
convergence to local minima. A large variety of methods for global optimization has been …

The purpose of this paper is to present a new and an alternative differential evolution (ADE)
algorithm for solving unconstrained global optimization problems. In the new algorithm, a …

A robust archived differential evolution algorithm is put forward by means of embedding a
flexibility processing operator and an efficiency processing operator based on original DE …

Global optimization is the task of finding the absolutely best set of parameters to optimize an
objective function. Generally, there are solutions that are locally optimal but not globally …

Global optimization problems continue to be a challenge in computational mathematics. The
field is progressing in two streams: deterministic and heuristic approaches. In this paper, we …

A derivative-free simulated annealing driven multi-start algorithm for continuous global
optimization is presented. We first propose a trial point generation scheme in continuous …