Numerical optimisation plays more and more important role in the antenna design. Because of lack of design-ready theoretical models, electromagnetic (EM)-simulation-driven adjustment of geometry parameters is a necess...
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Numerical optimisation plays more and more important role in the antenna design. Because of lack of design-ready theoretical models, electromagnetic (EM)-simulation-driven adjustment of geometry parameters is a necessary step of the design process. At the same time, traditional parameter sweeping cannot handle complex topologies and large number of designvariables. On the other hand, high computational cost of the conventional optimisation routines can be reduced using, e.g., surrogate-assisted techniques. Still, direct optimisation of EM simulation antenna models is required at certain level of fidelity. This work proposes a reduced cost trust-region algorithm with sparse updates of the antenna response Jacobian, decided based on relocation of the design variable vector between algorithm iterations and the update history. Our approach permits significant reduction of the optimisation cost (similar to 40% as compared to the reference algorithm) without affecting the design quality in a significant manner. Robustness of the proposed technique is validated using a set of benchmark antennas, statistical analysis of the algorithm performance over multiple initial designs, as well as investigating the effects of its control parameters that permit control efficiency vs. design quality trade-off. Selected designs were fabricated and measured to validate the computational models utilised in the optimisation process.
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