Expedited Feature-Based Quasi-Global Optimization of Multi-Band Antenna Input Characteristics With Jacobian Variability Tracking

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. INDEX TERMS Antenna design, input characteristics, EM-driven design, trust-region methods, response features.

Simulation-Driven Antenna Modeling by Means of Response Features and Confined Domains of Reduced Dimensionality

“…The concept of handling appropriately chosen characteristic points (features) of the response of device at hand rather than its entire responses (e.g., frequency characteristics) has been successfully employed in antenna design in various contexts, such as modeling [52], parametric optimization [53] or statistical analysis [54]. The motivation behind reformulating the design task in terms of the response features has come from the scrutiny of the dependence of the feature coordinates on the design variables, which appears to be much less nonlinear (in fact, often close to linear) than a similar dependence of the original responses (see Fig.…”

On Computationally-Efficient Reference Design Acquisition for Reduced-Cost Constrained Modeling and Re-Design of Compact Microwave Passives

“…As demonstrated (e.g., [65]- [67]), the relationship between the geometry parameters and the feature point coordinates is normally much less nonlinear than for the complete outputs (frequency characteristics), which allows for expediting the optimization procedures [66]. Another benefit of FBO is "flattening" of the cost function landscape, which often makes utilization of local algorithms sufficient in the cases that normally require global search [20]. For the sake of clarification, we consider an example branch-line coupler shown in Fig.…”

“…Surrogates are particularly useful for speeding up numerical procedures involving massive EM evaluations of the system under design. These include local [16], [17], and global parametric optimization [18]- [20], multi-criterial design [21]- [24], yield-driven optimization [25], or statistical analysis [26], [27]. There are two main groups of replacement models: approximation (or data-driven) [28], [29], and physics-based (e.g., space mapping [30], shapepreserving response prediction [31], etc.).…”

Expedited Yield Optimization of Narrow- and Multi-Band Antennas Using Performance-Driven Surrogates

“…Another option is the employment of the response feature approach [36], in which direct handling of original system characteristics (usually, S-parameters versus frequency) is replaced by constructing the surrogate at the level of suitably defined characteristic points. Reformulating the design task this way leads to a less nonlinear functional landscape, resulting in easier modeling that requires significantly smaller training data sets [37]- [39].…”