ABSTRACT
One of the virtues of phased arrays is the ability to create almost arbitrary far field pattern characteristics, by tailoring the amplitude and phase characteristics of the aperture, as enabled by the phase shifters and attenuators at each element. This chapter provides code and detailed examples of optimization using two variants of common stochastic optimizers, a particle swarm optimizer and a genetic algorithm. A range of permissible values for each amplitude and phase value is specified; for amplitude-only optimization, the range of amplitude values is 0 to 1, while the range for phase is 0 to 0 to enforce constant phase. Particle swarm optimization is very easy to understand and implement, with little computational bookkeeping. For the simultaneous optimization of N variables, a collection or swarm of particles is defined, where each particle is assigned a random position in the N-dimensional problem space so that each particle’s position corresponds to a candidate solution to the optimization problem.
