Rapid Multiobjective Antenna Design Using Point-By-Point Pareto Set Identification and Local Surrogate Models

Int J RF Microw Comput Aided Eng

2017

Practical engineering design problems are inherently multiobjective, that is, require simultaneous control of several (and often conflicting) criteria. In many situations, genuine multiobjective optimization is required to acquire comprehensive information about the system of interest. The most popular solution techniques are population‐based metaheuristics, however, they are not practical for handling expensive electromagnetic (EM)‐simulation models in microwave and antenna engineering. A workaround is to use auxiliary response surface approximation surrogates but it is challenging for higher‐dimensional problems. Recently, a deterministic approach has been proposed for expedited multiobjective design optimization of expensive models in computational EMs. The method relies on variable‐fidelity EM simulations, tracking the Pareto front geometry, as well as response correction. The algorithm sequentially generates Pareto‐optimal designs using a series of constrained single‐objective optimizations. The previously obtained design is used as a starting point for the next iteration. In this work, we review this technique and its modification based on space mapping surrogates. We also propose new variations exploiting adjoint sensitivities, as well as response features, which can be attractive depending on availability of derivatives or the characteristics of the system responses that need to be handled. We also discuss several case studies involving various antenna and microwave components.

“…This way, only a feasible part of the Pareto front (from the specification standpoint) is covered. There are various ways of establishing the sequence of thresholds

F_{2}^{(k)}

, for example, as

F_{2}^{(k)}

= α⋅

F_{2}^{(k - 1)}

with α < 1, or

F_{2}^{(k)}

F_{2}^{(k - 1)}

– with β > 0. Figure shows a conceptual illustration of the algorithm.…”

Section: Multiobjective Design Optimization By Pareto Front Explorationmentioning

confidence: 99%

“… Conceptual illustration of point‐by‐point exploration of the Pareto front using the algorithm (1–2) . The initial design (black circle) is determined using (1).…”

Section: Multiobjective Design Optimization By Pareto Front Explorationmentioning

confidence: 99%

“… Frequency responses of the selected UWB monopole antenna designs (for corresponding antenna dimensions, see Table 1)…”

Section: Design Case Studiesmentioning

confidence: 99%

“… Comparison of low‐fidelity Pareto‐optimal sets obtained using point‐by‐point algorithm (□), as well as MOEA optimization of R c (○) and kriging interpolation (×) models…”

Section: Design Case Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Recent advances in rapid multiobjective optimization of expensive simulation models in microwave and antenna engineering by Pareto front exploration

Int J RF Microw Comput Aided Eng

2017

“…, the approach has been extended to multidimensional problems using design‐space reduction algorithms. More recently, deterministic techniques have been proposed for rapid multiobjective optimization of microwave and antenna structures . However, none of these methods have been applied for analysis of design trade‐offs of CP antennas.…”

Section: Introductionmentioning

confidence: 99%

Analysis of circular polarization antenna design trade‐offs using low‐cost EM‐driven multiobjective optimization

Int J RF Microw Comput Aided Eng

Cheng

2018

Circular polarization (CP) antennas are vital components of modern communication systems. Their design involves handling several requirements such as low reflection and axial ratio (AR) within the frequency range of interest. Small size is an important criterion for antenna mobility which is normally achieved as a by‐product of performance‐oriented modifications of the structure topology. In this work, multiobjective optimization is used in order to identify and analyze design trade‐offs for miniaturized CP antenna including the antenna capability for maintaining small size while retaining acceptable levels of other performance figures. We use a population‐based metaheuristic algorithm to obtain a set of designs which represent the best attainable compromise between the imposed requirements. To maintain a low optimization cost, the algorithm is executed on a cheap approximation model and the results are further corrected to bring them to the EM model accuracy level. Here, the analysis is carried out for a planar CP antenna. Achievable size reduction of the considered structure—while maintaining acceptable performance—is around 11%. Antenna performance in terms of in‐band reflection and AR varies from −14 to −10 dB and from 1.3 to 3 dB, respectively. The numerical results are validated by measurements of fabricated antenna prototypes.

Precise control of reflection response in bandwidth-enhanced planar antennas

Int J RF and Microwave Comp Aid Eng

2016

In this work, the issues of bandwidth enhancement of planar antennas and the relevance of precise and automated response control through numerical optimization have been investigated. Using an example of a planar antenna with parasitic radiator we illustrate possible effects of even minor modifications of the antenna geometry (here, applied to the ground plane) on its reflection performance. In particular, a proper handling of geometry parameters may lead to considerable broadening of the antenna bandwidth. For the sake of computational efficiency, the adjustment of geometry parameters is carried out using surrogate‐based optimization methods exploiting coarse‐discretization EM simulations as the underlying low‐fidelity antenna model. Additionally, suitably defined penalty function allows us to precisely control the maximum in‐band reflection so that sufficient margin to accommodate possible manufacturing tolerances can be achieved. The optimized designs of the two antenna structures considered in this work exhibit over 1.75 GHz (>31%) and 2.15 GHz (>38%) bandwidth, respectively, for the center frequency of 5.6 GHz. Simulation results are validated using measurements of the fabricated prototypes. Comparison with state‐of‐the‐art designs is also provided. © 2016 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:653–659, 2016.