Computationally‐efficient design optimisation of antennas by accelerated gradient search with sensitivity and design change monitoring

Anna Pietrenko-Dabrowska, Slawomir Marcin Koziel

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Electromagnetic (EM) simulation tools are of primary importance in the design of contemporary antennas. The necessity of accurate performance evaluation of complex structures is a reason why the final tuning of antenna dimensions, aimed at improvement of electrical and field characteristics, needs to be based on EM analysis. Design automation is highly desirable and can be achieved by coupling EM solvers with numerical optimisation routines. Unfortunately, its computational overhead may be impractically high for conventional algorithms. This study proposes an efficient gradient search algorithm with numerical derivatives. The acceleration of the optimisation process is obtained by means of the two mechanisms developed to suppress some of finite-differentiation-based updates of the antenna response sensitivities that involve monitoring and quantifying the gradient changes as well as design relocation between the consecutive algorithm iterations. Both methods considerably reduce the need for finite differentiation, leading to significant computational savings. At the same time, excellent reliability and repeatability is maintained, which is demonstrated through statistics over multiple algorithm runs with random initial designs. The proposed approach is validated using a benchmark set of wideband antennas. The proposed algorithm is competitive to both the reference trust-region algorithm as well as its recently reported accelerated versions.
Original languageEnglish
Pages (from-to)165-170
JournalIET Microwaves, Antennas and Propagation
Volume14
Issue number2
DOIs
Publication statusPublished - 5 Feb 2020

Other keywords

  • Search problems
  • Optimisation
  • Gradient methods
  • Broadband antennas
  • Random initial designs
  • Wideband antennas
  • Reference trust-region algorithm
  • Accelerated gradient search

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