This thesis studies the effectiveness of bio-inspired optimization algorithms in
controlling adaptive antenna arrays. Smart antennas are able to automatically
extract the desired signal from interferer signals and external noise. The angular
pattern depends on the number of antenna elements, their geometrical arrangement,
and their relative amplitude and phases. In the present work different
antenna geometries are tested and compared when their array weights are optimized
by different techniques. First, the Genetic Algorithm and Particle Swarm
Optimization algorithms are used to find the best set of phases between antenna
elements to obtain a desired antenna pattern. This pattern must meet several
restraints, for example: Maximizing the power of the main lobe at a desired direction
while keeping nulls towards interferers. A series of experiments show that
the PSO achieves better and more consistent radiation patterns than the GA in
terms of the total area of the antenna pattern. A second set of experiments use
the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization
algorithms to find the array weights that configure a rectangular array. The results
suggest an advantage in performance by reducing the number of iterations
taken by the PSO, thus lowering the computational cost. During the development
of this thesis, it was found that the initial states and particular parameters of
the optimization algorithms affected their overall outcome. The third part of this
work deals with the meta-optimization of these parameters to achieve the best
results independently from particular initial parameters. Four algorithms were
studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing
and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal
Sampling and Pattern Search performed better to set the initial parameters
and obtain the best performance of the bio-inspired methods studied