The Mesh Adaptive Direct Search algorithm (MADS) is an iterative method for constrained blackbox optimization problems.
One of the optional MADS features is a versatile search step in which quadratic models are built leading to a series of quadratically constrained quadratic subproblems.
This work explores different algorithms that exploit the structure of the quadratic models: the first one applies an
\(l_1\) exact penalty function, the second uses an augmented Lagrangian and the third one combines the former two, resulting in a new algorithm. These methods are implemented within the NOMAD software package and their impact are assessed through computational experiments on 65 analytical test problems and 4 simulation-based engineering applications.
Paru en juin 2016 , 17 pages
Ce cahier a été révisé en novembre 2016