Exploiting non-linear transformations of the variables space in derivative-free global optimization

Solving a global optimization problem is a hard task. In this work we defined non-linear equations that are transformations between variable spaces, allowing us to tackle the problem advantageously. The idea is to gather the information obtained during a multi-start approach and to apply a sequence of transformations in the variables space that makes the exploration easier. The aim is to expand the attraction basins of global minimizers shrinking those of local minima we have already found.