Abstract
Design of modern antennas relies-for reliability reasons-on full-wave electromagnetic simulation tools. In addition, increasingly stringent specifications pertaining to electrical and field performance, growing complexity of antenna topologies, along with the necessity for handling multiple objectives, make numerical optimization of antenna geometry parameters a highly recommended design procedure. Conventional algorithms, particularly global ones, entail often-unmanageable computational costs, so alternative approaches are needed. This work proposes a novel method for cost-efficient globalized design optimization of multi-band antennas incorporating the response feature technology into the trust-region framework. It allows for unequivocal allocation of the antenna resonances even for poor initial designs, where conventional local algorithms fail. Furthermore, the algorithm is accelerated by means of Jacobian variability tracking, which reduces the number of expensive finite-differentiation updates. Two real-world antenna design cases are used for demonstration purposes. The optimization cost is comparable to that of local routines while ensuring nearly global search capabilities.
Highlights
Rapid development of cutting-edge technologies (e.g., 5G [1], internet of things [2], or wearable devices [3], including those for tele-medicine purposes [4]), leads to increasingly exacting requirements imposed on contemporary antenna structures
EM-driven design optimization in multidimensional parameter spaces is inevitably associated with massive EM simulations generating considerable CPU costs
The major contributions of this paper include: (i) incorporation of the response feature technology into trust-region gradient-based optimization framework, (ii) development of reduced-cost trust-region algorithm based on Jacobian variability monitoring, (iii) comprehensively demonstrated quasi-global search capabilities for multi-band antennas, (iv) demonstrated computational efficiency of the proposed framework, which is comparable to that of local search procedures
Summary
Rapid development of cutting-edge technologies (e.g., 5G [1], internet of things [2], or wearable devices [3], including those for tele-medicine purposes [4]), leads to increasingly exacting requirements imposed on contemporary antenna structures. A usable predictive power of the surrogate can only be secured if the design space is sampled with sufficient density, necessary to account for the system output variations within the model domain For this reason, usually large training data sets are required to construct functional surrogates, rapidly increasing with the number of antenna parameters (so called curse of dimensionality [25]). The major contributions of this paper include: (i) incorporation of the response feature technology into trust-region gradient-based optimization framework, (ii) development of reduced-cost trust-region algorithm based on Jacobian variability monitoring, (iii) comprehensively demonstrated quasi-global search capabilities for multi-band antennas, (iv) demonstrated computational efficiency of the proposed framework, which is comparable to that of local search procedures (dramatically lower than for routinely used population-based metaheuristics).
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