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 idependent schemes (Evolution Strategies, Simulated Annealing, Monte-Carlo Iteration) have been unified into a robust general method: Global Evolution Strategy (GES). Regularization techniques, stability of solutions and nonlinear phenomena are shown to be closely related topics to global optimization & inverse problems. Speed of convergence is evaluated for different optimization methods. A real-world application (from NMR & MRI) demonstrate the favourable behaviour of GES in the context of Finite Element Method.