Abstract
We present antenna switch optimizations using an efficient mechanism based on genetic algorithms and the multilevel fast multipole algorithm (MLFMA). Genetic algorithms are used to determine switch states for desired radiation and input characteristics, while cost-function evaluations are performed efficiently via an MLFMA implementation with dynamic error control. MLFMA is integrated into the genetic algorithm by extracting common computations to be performed once per optimization. Iterative convergence rates are further accelerated by using earlier solutions as initial-guess vectors. The efficiency of the developed mechanism is demonstrated on antennas with relatively large numbers of switches.
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