Surrogate-Assisted Level Set Topology Optimization for Pixelated Antenna Design

This letter proposes a novel antenna topology optimization framework consisting of two components. First, a Kriging-based level set method (KLSM) is employed to formulate the antenna optimization problem. The KLSM defines level set values at a small number of knot points and uses Kriging interpolation to generate the geometric representation of the antenna topology. The KLSM reduces the design space dimensionality and transforms the complex discrete pixelated antenna optimization problem into a low-dimensional continuous optimization problem via variable substitution. Second, a dual surrogate-assisted genetic algorithm (DSGA) is proposed to solve the KLSM-based antenna optimization problem. The DSGA employs two surrogate-assisted strategies to improve the traditional genetic algorithm. A local search is performed around the current best individual by using the radial basis function (RBF). A global search is performed by using expected improvement of high-dimensional Kriging to prescreen potential candidate individuals. The proposed surrogate-assisted topology optimization method is validated through the design of a pixelated monopole antenna. Results show that the proposed method outperforms the state-of-the-art methods in both convergence speed and optimization accuracy.