Low-cost multi-objective optimization of Yagi-Uda antenna in multi-dimensional parameter space

A surrogate-based technique for fast multi-objective optimization of a multi-parameter planar Yagi-Uda antenna structure is presented. The proposed method utilizes response surface approximation (RSA) models constructed using training samples obtained from evaluation of the low-fidelity antenna model. Utilization of the RSA models allows for fast determination of the best possible trade-offs between conflicting objectives in multi-objective optimization framework. Computationally feasible construction of the RSA model for the multi-parameter case is possible by initial reduction of the antenna solution space. The initial set of Pareto-optimal designs is obtained by optimizing the model with a multi-objective evolutionary algorithm (MOEA). Surrogate-based optimization is subsequently carried out to elevate the selected designs to the high-fidelity antenna model level. The refined Pareto optimal-set for the considered Yagi-Uda antenna is generated at the low computational cost corresponding to about 194 high-fidelity EM antenna simulations.