LAUSR

Trust-region (TR) algorithms represent a popular class of local optimization methods. Owing to straightforward setup and low computational cost, TR routines based on linear models determined using forward finite differences (FDs) are often utilized for performance tuning of microwave and antenna components incorporated within the Internet of Things (IoT) systems. Despite usefulness for design of complex structures, the performance of TR methods vastly depends on the quality of FD-based local models. The latter are normally identified from perturbations determined a priori using either rules-of-thumb, or as a result of manual tuning. In this work, a framework for the automatic determination of FD steps and their adjustment between the TR algorithm iterations is proposed. The method involves numerical optimization of perturbations so as to equalize the objective function changes w.r.t. the center design to the desirable precision. To maintain acceptable cost, the FD-tuning procedure is executed using the same approximation model as the one exploited in the course of the structure optimization. The proposed framework has been tested on a total of 12 design problems. Furthermore, the presented method has been thoroughly validated against TR algorithms with static, a priori selected perturbations. Numerical results indicate that the proposed framework provides up to 50% performance improvement (in terms of the optimized designs quality) compared to the state-of-the-art TR-based approaches. The usefulness of the proposed method for the real-world IoT systems has been implicitly demonstrated through the utilization of one of the optimized structures in a hardware layer of a real-time localization system.