The main aims of the proposed research activity are the study and the definition of new methodologies and algorithms for tackling optimization problems that present one or both of the following features:
- there are different objective functions which are in conflict with each other.
- the relationships between the variables and the values of the objective functions and some constraints are extremely complex and cannot be described analytically; in practice there exists only a ¿black box¿ (a simulation or approximation technique) which is able to provide sufficiently good approximations of the behaviors of the objective functions and constraints to vary the variables of the problems.
As regards the optimization problems showing the first feature (called Multiobjective Optimization Problems) the research activity will try to define new efficient and globally convergent minimization methods by drawing inspiration from the methodological approaches proposed for the single objective optimization problems.
The research activity concerning the Black Box Optimization Problems will be carried on along the definition of new algorithms which, using only function evaluations, are able to tackle difficult classes of black box optimization such as constrained global optimization problems, mixed optimization problems, nonsmooth optimization problems, bilevel optimization problems.
Furthermore the research group will face the very difficult challenge of giving methodological contributions for problems that are at the same time both Multiobjective and Black Box Optimization Problems
Finally, the new algorithms developed by the research activities will be used for solving difficult real problems deriving from optimal designs of electrical motors /electrical magnetic apparatus, optimal ship design problems, managements of healthcare services.
