A multi-objective genetic algorithm applied to array synthesis at multiple frequencies

Many complex electromagnetic designs require a multi-objective approach for the optimization as in the case of array synthesis. In this class of problems, there exists an ideal set of solutions, named the Pareto front, over which no other solution of the population dominates. Multi-objective genetic algorithms (MOAs) performs similarly to conventional genetic algorithms except that, rather than providing only a single optimal solution, the MOAs lead toward the determination of a Pareto front. This set presents a range of solutions for which no one can simultaneously satisfy the required performance but represents a tradeoff of the design requirements. Our aim is to develop a tool for the synthesis of array working at multiple frequencies within a large frequency band, with constraints imposed to the radiation pattern in terms of half-power beam width (HPBW) and side lobe level (SLL). More in detail, we are interested in developing an efficient code for more than two objectives. Therefore a particular version of multi-objective genetic algorithm, called nondominated sorting genetic algorithm (NSGA-II) has been implemented to optimize the array factor on the horizontal plane of a V-shaped array at three different frequencies. Multi-objective