Comparison of NSGA and ELM for finding the Pareto front of multiple-criteria antenna optimization problem

We compared two optimization algorithms for finding the Pareto front of the one-antenna optimization problem. The first applied algorithm is nondominated sorting genetic algorithm (NSGA) that has proved itself over other variants of GA for finding the Pareto front by the mean of effectiveness. The second applied algorithm is the multiminima optimization algorithm based on the estimation of local minima (ELM), which has been restarted for different weighting factors used for forming the single cost-function. The comparison between these two algorithms is done in the sense of the total number of iterations (EM solver runs) needed for finding a good estimation of the Pareto front. The goal was to find the Pareto front in the optimization of a Yagi antenna for the highest possible forward gain and lowest reflection coefficient in the frequency range 295-305 MHz.