Optimization of a Constrained Feed Network for an Antenna Array Using Simple and Competent Genetic Algorithm Techniques

This paper, which describes the optimization of a novel, constrained feed network for a space-based antenna array, is a joint effort between the Air Force Research Laboratory (AFRL) Antenna Technology Branch at Hanscom AFB and the Illinois Genetic Algorithms Laboratory (IlliGAL) at the University of Illinois at Urbana-Champaign. Recently, under the guidance and direction of the Air Force Office of Scientific Research (AFOSR), the two laboratories have formed a collaboration, the common goal of which is to apply simple, competent, and hybrid GA techniques to challenging antenna problems. As shown below, this particular optimization problem demonstrates the utility of using advanced GA techniques to obtain acceptable/enhanced solution quality. Figure 1 shows a single section of the antenna system, which consists of a front-end array and a constrained feed network. An incoming plane wave impinges the N -element linear array, and the resulting element excitations are propagated through an N byM Rotman lens, the outputs of which are weighted and fed into an M by M Butler Matrix. The center M/2 Butler outputs from each of P sections are time-delayed, weighted (e.g., fixed weights, like a Taylor distribution, etc.), and combined to compute the final radiation pattern of the system. The overall goal is to produce a far-field pattern having at least -30dB sidelobes over a 20% bandwidth by optimizing weights, wi (as shown in the figure), for P sections of the system. We applied both a simple genetic algorithm (SGA) and the hierarchical Bayesian optimization algorithm (hBOA) to a simulated model of the system in which the Rotman lens transfer functions (one for each of P sections) were