Accelerated multi-objective design of miniaturized microwave components by means of nested kriging surrogates - Publication - MOST Wiedzy


Accelerated multi-objective design of miniaturized microwave components by means of nested kriging surrogates


Design of microwave components is an inherently multiobjective task. Often, the objectives are at least partially conflicting and the designer has to work out a suitable compromise. In practice, generating the best possible trade‐off designs requires multiobjective optimization, which is a computationally demanding task. If the structure of interest is evaluated through full‐wave electromagnetic (EM) analysis, the employment of widely used population‐based metaheuristics algorithms may become prohibitive in computational terms. This is a common situation for miniaturized components, where considerable cross‐coupling effects make traditional representations (eg, network equivalents) grossly inaccurate. This article presents a framework for accelerated EM‐driven multiobjective design of compact microwave devices. It adopts a recently reported nested kriging methodology to identify the parameter space region containing the Pareto front and to render a fast surrogate, subsequently used to find the first approximation of the Pareto set. The final trade‐off designs are produced in a separate, surrogate‐assisted refinement process. Our approach is demonstrated using a three‐section impedance matching transformer designed for the best matching and the minimum footprint area. The Pareto set is generated at the cost of only a few hundred of high‐fidelity EM simulations of the transformer circuit despite a large number of geometry parameters involved.


  • 0


  • 0

    Web of Science

  • 0



artykuły w czasopismach
Published in:
ISSN: 1096-4290
Publication year:
Bibliographic description:
Pietrenko-Dąbrowska A., Kozieł S.: Accelerated multi-objective design of miniaturized microwave components by means of nested kriging surrogates// INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING -, (2020), s.1-11
Digital Object Identifier (open in new tab) 10.1002/mmce.22124
Bibliography: test
  1. Mohamed HAE, El-Shaarawy HB, Abdallah EA, El-Hennawy HMS. A very compact novel multi-band BPF for recent mobile/satellite communication systems. open in new tab
  2. Progr Electromagn Research C. 2014;50:47-56. open in new tab
  3. Sun L, Feng H, Li Y, Zhang Z. Compact 5G MIMO mobile phone antennas with tightly arranged orthogonal-mode pairs. IEEE Trans Antennas Propag. 2018;66(11);6364-6369. open in new tab
  4. Biswas AK, Chakraborty U. A compact wide band textile MIMO antenna with very low mutual coupling for wearable applications. Int J RF Microw Comput Aided Eng. 2019;29:e21769. open in new tab
  5. Shon A, Chu JU, Jung J, Kim H, Youn I. An implantable wireless neural interface system for simultaneous recording and stimulation of peripheral nerve with a single cuff electrode. Sensors. 2018;18(1):1-26. open in new tab
  6. Hamzah H, Abduljabar A, Lees J, Porch A. A compact microwave microfluidic sensor using a re-entrant cavity. Sensors. 2018;18(3):910. open in new tab
  7. Yang L, Zhou YJ, Zhang C, Yang XM, Yang X, Tan C. Compact multiband wireless energy harvesting based battery-free body area networks sensor for mobile healthcare. IEEE J Electromagn RF Microw Medicine Biology. 2018;2(2):109-115. open in new tab
  8. Zhang H, Chen X, Li M, Yang F, Xu S. A compact dual-band folded-cavity antenna for microwave biomedical imaging applications. IEEE Int Conf Comput Electromagn (ICCEM), Shanghai, China, 2019:1-3. open in new tab
  9. Mohamed M, Cheffena M, Moldsvor A, Fontan FP. Physical statistical channel model for off-body area network. IEEE Antennas Wireless Propag Lett. 2017;16:1516-1519. open in new tab
  10. Wagih M, Wei Y, Beeby S. Flexible 2.4 GHz node for body area networks with a compact high-gain planar antenna. IEEE Antennas Wireless Propag Lett. 2019;18(1):49-53. open in new tab
  11. Sarkhel A, Mitra D, Bhadra Chaudhuri SR. A compact metamaterial with multi- band negative-index characteristics. Appl Phys: A. 2016;122(4):471. open in new tab
  12. Shaw T, Mitra D. Wireless power transfer system based on magnetic dipole coupling with high permittivity metamaterials. IEEE Antennas Wireless Propag Lett. 2019;18(9):1823-1827. open in new tab
  13. Letavin D. Miniature microstrip branch line coupler with folded artificial transmission lines. Int J Electron Commun. 2019;99:pp. 8-13. open in new tab
  14. Coromina J, Vélez P, Bonache J, Aznar-Ballesta F, Fernández-Prieto A, Martín F.
  15. Reactively-loaded non-periodic slow-wave artificial transmission lines for stop band bandwidth enhancement: Application to power splitters. Int J Microwave Wireless Techn. 2019;11(5-6):475-481. open in new tab
  16. Kianinejad A, Chen ZN, Qiu C. Full modeling, loss reduction, and mutual coupling control of spoof surface plasmon-based meander slow wave transmission lines. open in new tab
  17. IEEE Trans Microwave Theory Techn. 2018;66(8):3764-3772. open in new tab
  18. Qian ZY, Chen JX. Compact bandpass filter using CMRC-based dual-behavior resonator. Int J RF Microw Comput Aided Eng. 2019;29:e21719. open in new tab
  19. Chen S, Guo M, Xu K, Zhao P, Dong L, Wang G. A frequency synthesizer based microwave permittivity sensor using CMRC structure. IEEE Access. 2018;6:8556- 8563. open in new tab
  20. Wang D, Chin K, Che W, Wu Y, Chang C. Compact 60 GHz low-temperature cofired ceramic filter with quasi-elliptic bandpass response. IET Microw Ant Propag. 2016;10(6):664-669. open in new tab
  21. Joseph N, Varghese J, Teirikangas M, Vahera T, Jantunen H. Ultra-low- temperature cofired ceramic substrates with low residual carbon for next-generation microwave applications. ACS Applied Materials Interfaces. 2019;11(26):23798- 23807. open in new tab
  22. Torun HM, Swaminathan M. High-dimensional global optimization method for high-frequency electronic design. IEEE Trans Microwave Theory Techn. 2019;67(6):2128-2142. open in new tab
  23. Wang D, Hu Y, Yue W, Zeng Y, Tu Z, Cai Y, Wang W, Fang Q, Yu M. Broadband and compact polarization beam splitter based on an asymmetrical directional coupler with extra optimizing designs. Appl Opt. 2019;58:8221-8226. open in new tab
  24. Zhang W, Feng F, Gongal-Reddy VWR, Zhang J, Yan S, Ma, Zhang QJ. Space mapping approach to electromagnetic centric multiphysics parametric modeling of microwave components. IEEE Trans Microwave Theory Techn. 2018;66(7):3169- 3185. open in new tab
  25. Koziel S, Leifsson L. Simulation-driven design by knowledge-based response correction techniques. Springer, Cham; 2016. open in new tab
  26. Koziel S, Bekasiewicz A. Fast simulation-driven feature-based design optimiza- tion of compact dual-band microstrip branch-line coupler. Int J RF Microwave CAE. 2015;26(1):13-20. open in new tab
  27. Feng F, Zhang C, Na W, Zhang J, Zhang W, Zhang QJ. Adaptive feature zero assisted surrogate-based EM optimization for microwave filter design. IEEE Microw Wireless Compon Lett. 2019;29(1):2-4. open in new tab
  28. Feng F, Zhang C, Zhang S, Gongal-Reddy VMR, Zhang QJ. Parallel EM optimization approach to microwave filter design using feature assisted neuro- transfer functions. IEEE MTT-S Int Microw Symp Dig, San Francisco, CA, 2016:1- 3. open in new tab
  29. Malhi H, Bakr MH. Geometry evolution of microwave filters exploiting self- adjoint sensitivity analysis. Int. Conf. Numerical Electromagn Multiphysics Mod Opt (NEMO), Ottawa, Canada, 2015. open in new tab
  30. Koziel S, Bekasiewicz A. Point-by-point Pareto front exploration and adjoint sensitivities for rapid multi-objective optimization of compact impedance matching transformers. Int J Numer Model. 2018;31(5):e2350. open in new tab
  31. Joung J. Machine learning-based antenna selection in wireless communications. IEEE Comm Lett. 2016;20(11):2241-2244. open in new tab
  32. Xiao L, Shao W, Ding X, Wang B. Dynamic adjustment kernel extreme learning machine for microwave component design. IEEE Trans Microwave Theory Techn. 2018;66(10):4452-4461. open in new tab
  33. Koziel S, Bekasiewicz A. Multi-objective design of antennas using surrogate models. World Scientific, Singapore; 2016. open in new tab
  34. Yilmaz T, Hasan N, Zane R, Pantic Z. Multi-objective optimization of circular magnetic couplers for wireless power transfer applications. IEEE Trans Magn. 2017;53(8):1-12. open in new tab
  35. Dong J, Li Q, Deng L, Fast multi-objective optimization of multi-parameter antenna structures based on improved MOEA/D with surrogate-assisted model, AEU Int J Electr Comm; 2017;72:192-199. open in new tab
  36. Koziel S, Bekasiewicz A. Strategies for computationally feasible multi-objective simulation-driven design of compact RF/microwave components. Eng Comput. 2016;33(1):184-201. open in new tab
  37. Markley L, Eleftheriades GV. An ultra-compact microstrip crossover inspired by contra-directional even and odd mode propagation. IEEE Microwave Wireless Comp Lett. 2014;24(7):436-438. open in new tab
  38. Pietrenko-Dabrowska A, Koziel S. Numerically efficient algorithm for compact microwave device optimization with flexible sensitivity updating scheme. Int J RF Microw Comput Aided Eng. 2019;29(7):e21714. open in new tab
  39. Nocedal J, Wright SJ. Numerical optimization. Springer Series in Operations Research, Springer; 2000. open in new tab
  40. Jin N, Rahmat-Samii Y. Advances in particle swarm optimization for antenna designs: realnumber, binary, single-objective and multiobjective implementations. IEEE Trans Ant Prop. 2007;55(3):556-567. open in new tab
  41. Feliot P, Bect J, Vazquez E. A Bayesian approach to constrained single-and multi- objective optimization. J Global Opt. 2017;67(1):1-37. open in new tab
  42. Koziel S, Bekasiewicz A, Zieniutycz W. Expedited EM-driven multi-objective antenna design in highly-dimensional parameter spaces. IEEE Ant Wireless Prop Lett. 2014;13:631-634. open in new tab
  43. Rao SS. Engineering Optimization: Theory and Practice. John Wiley & Sons; 2009.
  44. Koziel S, Bekasiewicz A. Fast EM-driven size reduction of antennastructures by means of adjoint sensitivities and trust regions. IEEE Ant Wireless Prop Lett. 2015;14:1681-1684. open in new tab
  45. Johanesson DO, Koziel S, Bekasiewicz A. EM-driven constrained miniaturization of antennas using adaptive in-band reflection acceptance threshold. Int J Numer Model. 2019;32(2):e2513. open in new tab
  46. Deb K. Multi-objective optimization using evolutionary algorithms. Wiley, New York; 2001 open in new tab
  47. Lalbakhsh A, Afzal MU, Esselle KP, Zeb BA. Multi-objective particle swarm optimization for the realization of a low profile bandpass frequency selective surface. 2015 Int Symp Antennas Propagation (ISAP). Hobart, TAS, 2015;1-4. open in new tab
  48. Zheng LM, Zhang SX, Zheng SY, Pan YM. Differential evolution algorithm with two-step subpopulation strategy and its application in microwave circuit designs. IEEE Trans Industrial Inf. 2016;12(3):911-923. open in new tab
  49. Baumgartner P, Bauernfeind T, Bíró O, Hackl A, Magele C, Renhart W, Torchio R. Multi-objective optimization of Yagi-Uda antenna applying enhanced firefly algorithm with adaptive cost function. IEEE Trans Magn. 2018;54(3):1-4. open in new tab
  50. Liu Y, Cheng QS, Koziel S. A generalized SDP multi-objective optimization method for EM-based microwave device design. Sensors, 2019;19:3065. open in new tab
  51. Nedjah N, Mourelle LM. Evolutionary multi-objective optimisation: a survey. Int J Bio-Inspir Comput. 2015;7(1)1-25. open in new tab
  52. Long Q, Wu C, Huang T, Wang X. A genetic algorithm for unconstrained multi- objective optimization. Swarm Evolut Comput. 2015;22:1-14. open in new tab
  53. Koziel S, Pietrenko-Dabrowska A. Performance-based nested surrogate modeling of antenna input characteristics. IEEE Trans Ant Prop. 2019;67(5):2904-2912. open in new tab
  54. Goudos SK, Diamantoulakis PD, Karagiannidis GK. Multi-objective optimization in 5G wireless networks with massive MIMO. IEEE Comm Lett. 2018;22(11):2346-2349. open in new tab
  55. Wang L, Wang G, Sidén J. Design of high-directivity wideband microstrip directional coupler with fragment-type structure. IEEE Trans Microwave Theory Techn. 2015;63(12):3962-3970. open in new tab
  56. Zheng SY, Zhang SX. A jumping genes inspired multi-objective differential evolution algorithm for microwave components optimization problems, Applied Soft Computing. 2017;59:276-287. open in new tab
  57. Koziel S, Bekasiewicz A, Kurgan P, Bandler JW. Rapid multi-objective design optimisation of compact microwave couplers by means of physics-based surrogates. IET Microw Ant Propag. 2016;10(5):479-486. open in new tab
  58. Koziel S, Bekasiewicz A, Kurgan P. Rapid multi-objective simulation-driven design of compact microwave circuits. IEEE Microwave Wireless Comp Lett. 2015;25(5):277-279. open in new tab
  59. Koziel, S, Bekasiewicz, A, Szczepanski, S. Multi-objective design optimization of antennas for reflection, size, and gain variability using kriging surrogates and generalized domain segmentation. Int J RF Microw Comput Aided Eng. 2018;28(5):e21253. open in new tab
  60. Couckuyt I, Declercq F, Dhaene T, Rogier H, Knockaert L. Surrogate-based infill optimization applied to electromagnetic problems. Int J RF and Microwave Comp Aid Eng, 2010;20(5):492-501. open in new tab
  61. Dong J, Qin W, Wang M. Fast multi-objective optimization of multi-parameter antenna structures based on improved BPNN surrogate model. IEEE Access. 2019;7:77692-77701. open in new tab
  62. Acampora G, Herrera F, Tortora G, Vitiello A. A multi-objective evolutionary approach to training set selection for support vector machine. Knowledge-Based Syst. 2018;147:94-108. open in new tab
  63. Kurgan P, Koziel S. Surrogate-assisted multi-objective optimization of compact microwave couplers. J Electromagn Waves App. 2016;30(15):2067-2075. open in new tab
  64. Martínez SZ, Coello CAC. Combining surrogate models and local search for dealing with expensive multi-objective optimization problems. 2013 IEEE Congress Evol Comput. Cancun, 2013:2572-2579. open in new tab
  65. Coello Coello CA. Evolutionary algorithms for solving multi-objective problems. Springer; 2007.
  66. Feng F, Zhang J, Zhang W, Zhao Z, Jin J, Zhang Q. Coarse-and fine-mesh space mapping for EM optimization incorporating mesh deformation. IEEE Microwave Wireless Comp Lett. 2019;29(8):510-512. open in new tab
  67. Koziel S, Ogurtsov S. Multi-objective design of antennas using variable-fidelity simulations and surrogate models. IEEE Trans Ant Propag. 2013;61(12):5931- 5939. open in new tab
  68. Koziel S, Bekasiewicz A. Rapid multiobjective antenna design using point-by-point Pareto set identification and local surrogate models. IEEE Trans Antennas Propag. 2016;64(6):2551-2556. open in new tab
  69. Koziel S, Bekasiewicz A. Rotational design space reduction for cost-efficient multi-objective antenna optimization. Europ Conf Ant Prop, Lisbon, Portugal, 2015:1-4. open in new tab
  70. Koziel S, Sigurdsson AT. Triangulation-based constrained surrogate modeling of antennas. IEEE Trans Ant Propag. 2017;66(8):4170-4179. open in new tab
  71. Simpson TW, Pelplinski JD, Koch PN, Allen JK. Metamodels for computer-based engineering design: survey and recommendations. Eng Computers. 2001;17:129- 150. open in new tab
  72. Koziel S, Bekasiewicz A. Rapid simulation-driven multi-objective design optimization of decomposable compact microwave passives. IEEE Trans Microwave Theory Techn. 2016;64:2454-2461. open in new tab
Verified by:
Gdańsk University of Technology

seen 9 times

Recommended for you

Meta Tags