A numerical comparison of some deterministic and nature-inspired algorithms for black-box global optimization

Many important design problems involve the search for the global extremum in the space of the system parameters. The functions to be optimized in many engineering applications are multi-extremal, behave as a black-box with unknown analytical representations, and are hard to evaluate. Therefore, it is necessary to use efficient methods of global finite-dimensional optimization for solving these challenging problems. In this paper, some of these methods developed by the authors in the framework of Lipschitz global optimization are briefly reviewed and compared with several widely-used nature-inspired algorithms