Efficient Sampling Strategy Driven Surrogate-Based Multi-Objective Optimization for Broadband Microwave Metamaterial Absorbers

Multi-objective optimization (MOO) for the microwave metamaterial absorber (MMA) normally adopts evolutionary algorithms, and these optimization algorithms require many objective function evaluations. To remedy this issue, a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions. An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations. Firstly, new sample points are generated by the MOO on surrogate models. Then, new samples are captured by exploiting each objective function. Furthermore, a weighted sum of the improvement of hypervolume (IHV) and the distance to sampled points is calculated to select the new sample. Compared with two well-known MOO algorithms, the proposed algorithm is validated by benchmark problems. In addition, two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.