Global optimization methods for computational electromagnetics

Both higher-order (pseudo)deterministic and zeroth-order probabilistic optimization methods have been analyzed and tested for solving the global optimization problems arising in computational electromagnetics. Previously recommended, but seemingly independent schemes (evolution strategies, simulated annealing, Monte Carlo iteration) have been unified into a robust general method: the global evolution strategy (GES). Regularization techniques, the stability of solutions, and nonlinear phenomena are shown to be topics closely related to global optimization and inverse problems. The speed of convergence is evaluated for different optimization methods. A real-world application (from nuclear magnetic resonance and magnetic resonance imaging) demonstrates the favorable behavior of GES in the context of the finite element method