access icon free Reduced-cost electromagnetic-driven optimisation of antenna structures by means of trust-region gradient-search with sparse Jacobian updates

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 design variables. 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 (∼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.

Inspec keywords: sparse matrices; Jacobian matrices; gradient methods; search problems; statistical analysis; optimisation; electromagnetic field theory; antenna theory

Other keywords: multiple initial designs; reduced cost trust-region algorithm; design quality trade-off; sparse Jacobian updates; design variable vector; parameter sweeping; electromagnetic-simulation-driven adjustment; statistical analysis; antenna parameterisation; EM simulation antenna models; direct optimisation; complex topologies; design-ready theoretical models; sparse updates; geometry parameters; numerical optimisation; high computational cost reduction; antenna systems; surrogate-assisted techniques; trust-region gradient-search; design process; benchmark antenna structures; electromagnetic-driven optimisation; reference algorithm; antenna response Jacobian; control parameters

Subjects: Algebra; Antenna theory; Optimisation techniques; Combinatorial mathematics; Other topics in statistics

References

    1. 1)
      • 6. Lalbakhsh, A., Afzal, M.U., Esselle, K.P.: ‘Multiobjective particle swarm optimization to design a time-delay equalizer metasurface for an electromagnetic band-gap resonator antenna’, IEEE Antennas Wireless Propag. Lett., 2017, 16, pp. 912915.
    2. 2)
      • 23. Alsath, M.G.N., Kanagasabai, M.: ‘‘Compact UWB monopole antenna for automotive communications’, IEEE Trans. Antennas Propag., 2015, 63, (9), pp. 42044208.
    3. 3)
      • 19. Koziel, S., Bekasiewicz, A.: ‘Statistical analysis and robust design of circularly polarized antennas using sequential approximate optimization’. IEEE Int. Microwave and Radar Conf. (MIKON), Poznan, Poland, May 2018, pp. 424427.
    4. 4)
      • 13. Koziel, S., Leifsson, L.: ‘Simulation-driven design by knowledge-based response correction techniques’ (Springer, Cham, Switzerland, 2016).
    5. 5)
      • 21. Chiu, Y.H., Chen, Y.S.: ‘Multi-objective optimization of UWB antennas in impedance matching, gain, and fidelity factor’. IEEE Int. Symp. Ant. Prop., Vancouver, BC, Canada, July 2015, pp. 19401941.
    6. 6)
      • 1. Koziel, S., Ogurtsov, S.: ‘Antenna design by simulation-driven optimization. Surrogate-based approach’ (Springer, New York, 2014).
    7. 7)
      • 2. Nocedal, J., Wright, S.J.: ‘Numerical optimization’ (Springer, New York, 2006, 2nd edn).
    8. 8)
      • 4. Soltani, S., Lotfi, P., Murch, R.D.: ‘Design and optimization of multiport pixel antennas’, IEEE Trans. Antennas Propag., 2018, 66, (4), pp. 20492054.
    9. 9)
      • 17. de Villiers, D.I.L., Couckuyt, I., et al: ‘Multi-objective optimization of reflector antennas using kriging and probability of improvement’. Int. Symp. Ant. Prop., San Diego, USA, 2017, pp. 985986.
    10. 10)
      • 7. Ghassemi, M., Bakr, M., Sangary, N.: ‘Antenna design exploiting adjoint sensitivity-based geometry evolution’, IET Microw. Antennas Propag., 2013, 7, (4), pp. 268276.
    11. 11)
      • 10. Koziel, S., Bekasiewicz, A.: ‘Multi-objective design of antennas using surrogate models’ (World Scientific, London, UK, 2016).
    12. 12)
      • 3. Kolda, T.G., Lewis, R.M., Torczon, V.: ‘Optimization by direct search: new perspectives on some classical and modern methods’, SIAM Rev., 2003, 45, (3), pp. 385482.
    13. 13)
      • 22. Conn, A.R., Gould, N.I.M., Toint, P.L.: ‘Trust region methods, MPS-SIAM series on optimization’, 2000.
    14. 14)
      • 12. Koziel, S., Ogurtsov, S.: ‘Design optimization of antennas using electromagnetic simulations and adaptive response correction technique’, IET Microw. Antennas Propag., 2014, 8, (3), pp. 180185.
    15. 15)
      • 18. Jacobs, J.P.: ‘Characterization by Gaussian processes of finite substrate size effects on gain patterns of microstrip antennas’, IET Microw. Antennas Propag., 2016, 10, (11), pp. 11891195.
    16. 16)
      • 25. Suryawanshi, D.R., Singh, B.A.: ‘A compact UWB rectangular slotted monopole antenna’. IEEE Int. Conf. Control, Instrumentation, Comm. Comp. Tech. (ICCICCT), Kanyakumari, India, July 2014, pp. 11301136.
    17. 17)
      • 20. Koziel, S., Cheng, Q.S., Li, S.: ‘Optimization-driven antenna design framework with multiple performance constraints’, Int. J. RF Microw. Comput. Aided Eng., 2018, 28, (4), pp. 111.
    18. 18)
      • 11. Zhu, J., Bandler, J.W., Nikolova, N.K., et al: ‘Antenna optimization through space mapping’, IEEE Trans. Antennas Propag., 2007, 55, (3), pp. 651658.
    19. 19)
      • 15. Koziel, S., Unnsteinsson, S.D.: ‘Accelerated design optimization of antenna structures using adaptive response scaling’. IEEE Int. Symp. Ant. Prop., Boston, MA, USA, July 2018, pp. 791792.
    20. 20)
      • 5. Goudos, S.K., Siakavara, K., Samaras, T., et al: ‘Self-adaptive differential evolution applied to real-valued antenna and microwave design problems’, IEEE Trans. Antennas Propag., 2011, 59, (4), pp. 12861298.
    21. 21)
      • 14. Koziel, S.: ‘Fast simulation-driven antenna design using response-feature surrogates’, Int. J. RF Microw. Comput. Aided Eng., 2015, 25, (5), pp. 394402.
    22. 22)
      • 9. CST Microwave Studio, ver. 2015, CST AG, Bad Nauheimer Str. 19, D-64289 Darmstadt, Germany, 2015.
    23. 23)
      • 24. Haq, M.A., Koziel, S., Cheng, Q.S.: ‘EM-driven size reduction of UWB antennas with ground plane modifications’. Int. Applied Comp. Electromagnetics Society (ACES China) Symp., Suzhou, China, August 2017, pp. 12.
    24. 24)
      • 8. Koziel, S., Mosler, F., Reitzinger, S., et al: ‘Robust microwave design optimization using adjoint sensitivity and trust regions’, Int. J. RF Microw. Comput. Aided Eng., 2012, 22, (1), pp. 1019.
    25. 25)
      • 16. Liu, B., Koziel, S., Zhang, Q.: ‘A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems’, J. Comput. Sci., 2016, 12, pp. 2837.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-map.2018.5879
Loading

Related content

content/journals/10.1049/iet-map.2018.5879
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading