Dual-objective Design of Multilayer Radar Absorbing Composite Material Using Butterfly Optimization Algorithm


Toktaş A., Ustun D.

IEEE 25th International Seminar / Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory (DIPED), Tbilisi, Gürcistan, 15 - 18 Eylül 2020, ss.77-81 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/diped49797.2020.9273392
  • Basıldığı Şehir: Tbilisi
  • Basıldığı Ülke: Gürcistan
  • Sayfa Sayıları: ss.77-81
  • Anahtar Kelimeler: radar absorbing material (RAM), multilayer radar absorbing material, multi-objective optimization, butterfly optimization algorithm, composite, MICROWAVE-ABSORPTION PROPERTIES, ELECTROMAGNETIC PROPERTIES, ABC ALGORITHM, LIGHTWEIGHT, EVOLUTION, IRON
  • Ankara Üniversitesi Adresli: Evet

Özet

A Multilayer Radar Absorbing Material (MRAM) based on composite is designed using a dual-objective optimization method butterfly optimization algorithm (BOA). The two objective functions regard Total Reflection (TR) involving sub-reflection at the inner layers and Total Thickness (TT). In order to make the MRA material more applicable for real applications; first, Mean Oblique Incidence (MOI) of TR in the range of 0-60 degree with polarizations TE/TM and then the average of MOI-TR in the range of 2-18 GHz is taken for achieving MOI-TRavg. The design variables, i.e. the thickness and material specimens for each layer from a material database including 21 composite specimens is optimally determined for synchronously minimizing the two objectives. Thus, a Global optimum MRAM (GMRAM) is picked out within the distributed candidate optimal solution set through Pareto optimality. Therefore, a five-layer GMRAM effectively absorbing at 7-18 GHz is successfully determined with the objectives of -10.85 dB MOI-TRavg and 4.2493 mm TT by considering the trade-off between the two objectives. The GMRAM is compared with a respective design in the literature to demonstrate the effectiveness of proposed optimization method. The TT is reduced as 66% by keeping the TR performance almost the same thanks to the effective and versatile dual-objective optimization with BOA.