SADEA-II: A generalized method for efficient global optimization of antenna design

Bo Liu, Slawomir Koziel, Nazar Ali

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality.

Original languageBritish English
Pages (from-to)86-97
Number of pages12
JournalJournal of Computational Design and Engineering
Volume4
Issue number2
DOIs
StatePublished - 1 Apr 2017

Keywords

  • Antenna design automation
  • Antenna design optimization
  • Expensive optimization
  • Gaussian process
  • Multi-fidelity
  • Surrogate-model-assisted evolutionary algorithm
  • Variable fidelity

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