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This paper addresses computationally feasible multi-objective optimization of antenna structures. We review two recent techniques that utilize the multi-objective evolutionary algorithm (MOEA) working with fast antenna replacement models (surrogates) constructed as Kriging interpolation of coarse-discretization electromagnetic (EM) simulation data. The initial set of Pareto-optimal designs is subsequently refined to elevate it to the high-fidelity EM simulation accuracy. In the first method, this is realized point-by-point through appropriate response correction techniques. In the second method, sparsely sampled high-fidelity simulation data is blended into the surrogate model using Co-kriging. Both methods are illustrated using two design examples: an ultra-wideband (UWB) monocone antenna and a planar Yagi-Uda antenna. Advantages and disadvantages of the methods are also discussed. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) simulation has become an important tool in the design of contemporary antenna structures. However, accurate simulations of realistic antenna models are expensive and therefore design automation by employing EM solver within an optimisation loop may be prohibitive because of its high computational cost. Efficient EM-driven antenna design can be performed using surrogate-based optimisation (SBO). A generic approach to construct surrogate models of antennas involves the use of coarse-discretisation EM simulations (low-fidelity models). A proper selection of the surrogate model fidelity is a key factor that influences both the performance of the design optimisation process and its computational cost. Despite its importance, this issue has not yet been investigated in the literature. Here, the authors focus on a problem of proper surrogate model management. More specifically, the authors carry out a numerical study that aims at finding a trade-off between the design cost and reliability of the SBO algorithms. Our considerations are illustrated using several antenna design cases. Furthermore, the authors demonstrate that the use of multiple models of different fidelity may be beneficial to reduce the design cost while maintaining the robustness of the optimisation process. Recommendations regarding the selection of the surrogate model coarseness are also given. [ABSTRACT FROM AUTHOR]

A technique for design optimisation of antennas is presented. The approach exploits coarse-discretisation electromagnetic (EM) simulations of the antenna of interest that are used to create its fast initial model (a surrogate) through Kriging. During the design process, predictions obtained by optimising the surrogate are verified using high-fidelity EM simulations, and these high-fidelity data are used to enhance the surrogate using co-Kriging interpolation that accommodates all EM simulation data into one surrogate model. The co-Kriging-based optimisation algorithm is simple and it is capable of yielding a satisfactory design at a low-cost equivalent to a few high-fidelity EM simulations of the antenna under design. To the best of author's knowledge, this is a first application of co-Kriging to antenna design. The proposed approach is demonstrated using several antenna design cases and compared with other techniques including pattern search and space mapping. [ABSTRACT FROM AUTHOR]

A two-level derivative-free algorithm for electromagnetic-simulation-driven design optimisation is introduced. The technique exploits a fast surrogate model that is created using a modified version of a shape-preserving response prediction (SPRP) methodology and a limited amount of coarse-discretisation electromagnetic (EM) simulation data. The surrogate is subsequently utilised by the space mapping algorithm to find a design satisfying given performance specifications. As demonstrated using two examples of microstrip filters and a planar antenna, the design cost is low and corresponds to a few high-fidelity EM simulations of the optimised structure and it is 30-50% lower than that for the original SPRP technique exploiting coarse-discretisation EM models. [ABSTRACT FROM AUTHOR]

Tuning space mapping (TSM) with embedded tuning elements is a recent development in space mapping technology. The process allows efficient design optimisation of microwave components by substituting sections in an electromagnetic model with corresponding sections of designable equivalent elements. Available parameters of these elements are subsequently tuned and their optimal values are 'translated' into relevant changes in the original structure's design variables. Although the TSM optimisation process typically requires only a few iterations to complete, the simulation of the structure with a number of co-calibrated ports (required for subsequent insertion of the tuning elements) can be substantially longer than that of the original structure. This results in substantial computational overhead of the design process. Here, the authors review a fast TSM algorithm that exploits a reduced structure with significantly fewer co-calibrated ports for creating the tuning model and allows us to obtain dramatic reduction of the computational cost of the optimisation process. In a design framework, the fast TSM as well as other TSM methods are implemented and automated in space mapping framework (SMF) software. Comprehensive verification and comparison with the standard TSM are provided through several examples of microstrip filters. [ABSTRACT FROM AUTHOR]

An improved technique for accurate modelling of microwave structures is presented. It is a modification of a recently introduced approach exploiting shape-preserving response prediction (SPRP) procedure that estimated the response of the CPU-intensive EM-simulated microwave structure using an auxiliary and computationally cheap model of the structure and a single reference design. Here, the response is estimated using multiple reference designs, which allows better utilisation of available training data. It is demonstrated that the proposed approach offers excellent modelling accuracy, which is not only better than the accuracy provided by space mapping but also better than the accuracy of the original modelling using SPRP. The authors approach is particularly suitable to build low-cost models useful to perform design tasks such as parametric optimisation and statistical analysis. [ABSTRACT FROM AUTHOR]

Space mapping (SM) is probably one of the most efficient simulation-driven design methodologies used in microwave engineering to date. Nevertheless, its performance heavily depends on the proper selection of the surrogate model, a computationally cheap representation of the microwave structure under consideration, which is the key component of the SM algorithm. Despite some attempts of automating the set-up of the surrogate model and its parameters, the successful application of SM still requires some user experience. In this work, an efficient method to alleviate this problem is discussed that exploits adaptively constrained parameter extraction and surrogate optimisation processes. As a result, improved convergence properties and overall performance of the SM algorithm are observed. The proposed technique is verified through the design of several microstrip filters. It is also compared with standard SM as well as SM enhanced by a trust-region methodology. [ABSTRACT FROM AUTHOR]

The authors present efficient space mapping (SM) modelling of microwave devices where they assume a tight limit on the number of high-fidelity electromagnetic (EM) simulations available to set up a suitable model. The authors' method exploits a two-stage procedure. In the first stage, the standard SM surrogate is enhanced by a functional approximation layer using coarse-mesh EM simulation data. In the second stage, this surrogate is updated through space-mapping-based matching to high-fidelity EM simulation responses at a limited number of base locations (designs). The first stage normally uses a physically based coarse model, but in some cases direct approximation of the coarse-mesh EM simulation data is sufficient. The authors demonstrate that their proposed approach improves modelling accuracy in comparison with standard SM, and no extra fine model evaluations are required to set up the model. Applications to parametric design optimisation are discussed. [ABSTRACT FROM AUTHOR]

A novel implementation of a space-mapping (SM) algorithm for optimisation of microwave structures and devices is described. The algorithm uses two techniques to speed up the SM optimisation process: the evaluation of the fine model is distributed through independent processing of the fine model response corresponding to consecutive frequency samples using a number of CPUs, and the parameter extraction and surrogate optimisation sub-problems are solved using built-in optimisation capabilities of the coarse model simulator. As a result, the optimisation time of microwave structures can be reduced to values comparable to or smaller than the time necessary for a single fine model evaluation on a single processor. This new implementation can be applied whenever the fine model is evaluated using a frequency-domain simulator. The robustness of this algorithm using microwave design optimisation problems is verified. The efficiency is compared with the standard implementation of the SM algorithm. [ABSTRACT FROM AUTHOR]