Pareto Optimal Yagi-Uda Antenna Design Using Multi-Objective Differential Evolution

Antenna design problems often require the optimization of several conflicting objectives such as gain maximization, sidelobe level (SLL) reduction and input impedance matching. Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. An efficient algorithm is Generalized Differential Evolution (GDE3), which is a multi-objective extension of Differential Evolution (DE). The GDE3 algorithm can be applied to global optimization of any engineering problem with an arbitrary number of objective and constraint functions. Another popular MOEA is Nondominated Sorting Genetic Algorithm-II (NSGA-II). Both GDE3 and NSGA-II are applied to Yagi-Uda antenna design under specified constraints. The numerical solver used for antenna parameters calculations is SuperNEC, an object-oriented version of the numerical electromagnetic code (NEC-2). Three different Yagi-Uda antenna designs are considered and optimized. Pareto fronts are produced for both algorithms. The results indicate the advantages of this approach and the applicability of this design method.