Abstract

Purpose–The purpose of this paper is to validate methodologies for expedited multi-objective designoptimization of complex antenna structures both numerically and experimentally.Design/methodology/approach–The task of identifying the best possible trade-offs between theantenna size and its electrical performance is formulated as multi-objective optimization problem.Algorithmic frameworks are described for finding Pareto-optimal designs using auxiliary surrogatemodels and two alternative approaches to design refinement: response correction techniques andco-kriging. Numerical and experimental case studies are provided to demonstrate feasibility of solvingreal-world and complex antenna design tasks.Findings–It is possible, through appropriate combination of the surrogate modeling techniques (bothdata driven and physics based) and response correction methods, to find the set of alternative designsrepresenting the best possible trade-offs between conflicting design objectives, here, electricalperformance and size. Design optimization can be performed at practically feasible computational costs.Research limitations/implications–The study demonstrates feasibility of automated multi-objectivedesign optimization of antennas at low computational cost. The presented techniques reach beyond thecommonly used design approaches based on parameter sweeps and similar hands-on methods, particularlyin terms of automation, reliability and reduction of the computational costs of the design processes.Originality/value–Multi-objective design of antenna structures is very challenging when high-fidelity electromagnetic simulations are utilized for performance evaluation of the structure at hand.The proposed design framework permits rapid optimization of complex structures (here, MIMOantenna), which is hardly possible using conventional methods.

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