Chapter 7: Multi-Objective Antenna Optimization Using Surrogate Models

Nowadays, the most popular approaches for solving multi-objective optimization problems are population-based metaheuristics. Their important advantage is the ability of generating the entire representation of the Pareto front in a single run of the algorithm. Other advantages include simplicity and availability of numerous (and often reliable) implementations. A brief exposition of these methods was provided in Chapter 6. On the other hand, a serious disadvantage of population-based algorithms is their considerable computational complexity. In a typical multi-objective setup, a large population size is utilized (from a 100 to a few hundreds of individuals) so that the overall number of objective function evaluations during the optimization run might be as high as a few thousands to tens of thousands. This becomes a fundamental bottleneck when applying metaheuristics for multi-objective design of contemporary antenna structures. As explained in Chapter 2, reliable performance evaluation of antennas requires full-wave electromagnetic (EM) analysis. Such analysis may be quite expensive particularly for realistic models (e.g., including connectors and installation fixtures) with typical simulation times of several minutes to a few hours per design. Clearly, straightforward optimization of EM models using population-based algorithms might be prohibitive…