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Fast multi-objective design optimization of compact microstrip impedance transformers is discussed. Our approach exploits approximation models constructed using sampled coarse-mesh EM simulation data in a partitioned design space and response correction techniques for design refinement. Demonstration example of a 50-to-130 ohm wideband transformer is provided.
In this paper, a novel technique for fast multi-objective design optimization of antenna structures has been presented. In our approach, the initial approximation of the Pareto front is obtained using a bisection algorithm which performs sequential partitioning of the design space and refines the new designs by means of poll-type search involving Pareto ranking. This stage of the optimization process...
In this paper, a low-cost formulation of space mapping (SM) for design optimization of antenna structures is investigated. The major challenge is that low-fidelity (or coarse) antenna models are normally obtained from coarse-discretization EM simulations so that their evaluation cost cannot be neglected. Consequently, the SM algorithm has to aim at reducing both the number of fine and coarse model...
A numerical study concerning multi-objective optimization of antenna structures using sequential domain patching (SDP) technique has been presented. We investigate the effect of various setups of the patch size on the operation of the SDP algorithm and possible trade-offs concerning the quality of the Pareto set found by SDP and the computational cost of the optimization process. Our considerations...
In the paper, a procedure for low-cost multi-objective optimization of antenna structures is presented. Our approach is based on exploration of the Pareto front representing the best possible trade-offs between conflicting objectives, here, the structure size and its electrical performance. Starting from the design representing the best in-band reflection level, subsequent Pareto-optimal designs are...
A methodology for expedited simulation-based design of microstrip linear antenna arrays is presented. Our approach utilizes response-corrected array factor model as well as surrogate-based optimization of the array elements for simultaneous control of both the radiation and reflection responses of the array. The technique is demonstrated using a 16-element array of slot-fed microstrip patch antennas.
A fast optimization procedure for EM-driven design of narrow-band antennas is presented. Our approach exploits local approximation models of the antenna response, based on a set of suitably selected characteristic features rather than the entire responses (as functions of frequency). The optimization process is embedded in the trust-region framework for safeguarding convergence. The proposed technique...
Feasible multi-objective optimization of antenna structures is presented. An initial set of Pareto optimal solutions is found using a multi-objective evolutionary algorithm (MOEA) working with a fast surrogate antenna model obtained by kriging interpolation of coarse-discretization EM simulation data. To make the surrogate construction computationally feasible in multi-dimensional design space, the...
Microwave design optimization using variable-fidelity electromagnetic (EM) simulations and multi-point response correction is presented. In our approach, optimization of the computationally expensive high-fidelity EM model is replaced by iterative design improvement of the low-fidelity model, which is obtained from coarse-discretization EM simulation data. The low-fidelity model is enhanced through...
A robust space mapping algorithm for microwave design optimization is presented. Our approach utilizes low-cost derivative information obtained through adjoint sensitivities to improve the matching between a high-fidelity model evaluated using expensive EM simulation and its surrogate. Here, the surrogate is obtained through coarse-discretization EM simulation (low-fidelity model). Adjoint sensitivities...
A simplified schematic is presented to implement fast and accurate space-mapping-based modeling and design. It can match a surrogate model with both fine model responses and approximated responses. The implementation allows us to study aspects of modeling, including sample selection, time cost and accuracy. A nominal design is obtained with selected models and verified through high-fidelity EM simulations...
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