A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design - Publication - MOST Wiedzy


A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design


In this article, a generalized sequential domain patching (GSDP) method for efficient multi-objective optimization based on electromagnetics (EM) simulation is proposed. The GSDP method allowing fast searching for Pareto fronts for two and three objectives is elaborated in detail in this paper. The GSDP method is compared with the NSGA-II method using multi-objective problems in the DTLZ series, and the results show the GSDP method saved computational cost by more than 85% compared to NSGA-II method. A diversity comparison indicator (DCI) is used to evaluate approximate Pareto fronts. The comparison results show the diversity performance of GSDP is better than that of NSGA-II in most cases. We demonstrate the proposed GSDP method using a practical multi-objective design example of EM-based UWB antenna for IoT applications.


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artykuł w czasopiśmie wyróżnionym w JCR
Published in:
SENSORS no. 19, pages 1 - 16,
ISSN: 1424-8220
Publication year:
Bibliographic description:
Ying L., Cheng Q., Kozieł S.: A Generalized SDP Multi-Objective Optimization Method for EM-Based Microwave Device Design// SENSORS. -Vol. 19, iss. 14 (2019), s.1-16
Digital Object Identifier (open in new tab) 10.3390/s19143065
Bibliography: test
  1. Chadebec, O.; Coulomb, J.L.; Janet, F. A review of magnetostatic moment method. IEEE Trans. Magn. 2006, 42, 515-520. [CrossRef] open in new tab
  2. Wu, Y.; Wassell, I. Introduction to the Segmented Finite-Difference Time-Domain Method. IEEE Trans. Magn. 2009, 45, 1364-1367.
  3. Dennis, J.E. Quasi-Newton Methods, Motivation and Theory. Siam Rev. 1977, 19, 46-89. [CrossRef] open in new tab
  4. Konak, A.; Coit, D.W.; Smith, A.E. Multi-objective optimization using genetic algorithms: A tutorial. Reliab. Eng. Syst. Saf. 2006, 91, 992-1007. [CrossRef] Sensors 2019, 19, 3065 open in new tab
  5. Koziel, S.; Bekasiewicz, A.; Zieniutycz, W. Expedited EM-Driven Multiobjective Antenna Design in Highly Dimensional Parameter Spaces, Motivation and Theory. IEEE Antennas Wirel. Propag. Lett. 2014, 13, 631-634. [CrossRef] open in new tab
  6. Hsieh, L.H.; Chang, K. Compact, low insertion-loss, sharp-rejection, and wide-band microstrip bandpass filters. IEEE Trans. Microw. Theory Tech. 2003, 51, 1241-1246. [CrossRef] open in new tab
  7. Koziel, S.; Ogurtsov, S. Multi-Objective Design of Antennas Using Variable-Fidelity Simulations and Surrogate Models. IEEE Trans. Antennas Propag. 2013, 61, 5931-5939. [CrossRef] open in new tab
  8. Coello, C.A.C. Evolutionary Multi-Objective Optimization a Historical View of the Field. IEEE Comput. Int. Mag. 2006, 1, 28-36. [CrossRef] open in new tab
  9. Rütschlin, M.; Wittig, T. State of the Art Antenna Simulation with CST STUDIO SUITE. In Proceedings of the 2015 9th European Conference on Antennas and Propagation, Lisbon, Portugal, 7-12 April 2015. open in new tab
  10. Dar, S.H.; Ahmed, Z.; Ihsan, M.B. Characterization of Waveguide Slots Using Full Wave EM Analysis Software HFSS. In Proceedings of the 2008 IEEE International Multitopic Conference, Karachi, Pakistan, 23-24 December 2008. open in new tab
  11. Torczon, V. On the Convergence of Pattern Search Algorithms. SIAM J. Optim. 1997, 7, 1-25. [CrossRef] open in new tab
  12. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6, 182-197. [CrossRef] open in new tab
  13. Sha, D.; Lin, H.-H. A multi-objective PSO for job-shop scheduling problems. Expert Syst. Appl. 2010, 37, 1065-1070. [CrossRef] open in new tab
  14. Leal-Romo, F.; Moreyra-González, R.; Rayas-Sánchez, J.E. HFSS Automated Driver Based on Non-GUI Scripting for EM-Based Design of High-Frequency Circuits. In Proceedings of the 2012 IEEE 3rd Latin American Symposium on Circuits and Systems (LASCAS), Playa del Carmen, Mexico, 29 February-2 March 2012. open in new tab
  15. Bandler, J.W.; Cheng, Q.S.; Gebre-Mariam, D.H.; Madsen, K.; Pedersen, F.; Sondergaard, J. EM-Based Surrogate Modeling and Design Exploiting Implicit, Frequency and Output Space Mappings. In Proceedings of the IEEE MTT-S International Microwave Symposium Digest, Philadelphia, PA, USA, 10-15 July 2003. open in new tab
  16. Rayas-Sánchez, J.E.; Estrada-Arámbula, E. EM-Based Design Optimization of Microstrip Lines Traversing a Rectangular Gap in the Reference Plane. In Proceedings of the 2012 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), Seville, Spain, 19-21 September 2012. open in new tab
  17. Koziel, S.; Sigurasson, A.T. Multi-Objective Design of Antennas Using Variable-Fidelity EM Models and Constrained Surrogates. In Proceedings of the 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, Boston, MA, USA, 8-13 July 2018. open in new tab
  18. Koziel, S.; Ogurtsov, S.; Zieniutycz, W.; Bekasiewicz, A. Design of a Planar UWB Dipole Antenna with an Integrated Balun Using Surrogate-Based Optimization. IEEE Antennas Wirel. Propag. Lett. 2015, 14, 366-369. [CrossRef] open in new tab
  19. Akinsolu, M.O.; Liu, B.; Grout, V.; Lazaridis, P.I.; Mognaschi, M.E.; Di Barba, P. A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization. IEEE Trans. Emerg. Top. Comput. Intell. 2019, 3, 93-105. [CrossRef] open in new tab
  20. Feng, F.; Zhang, C.; Na, W.; Zhang, J.; Zhang, W.; Zhang, Q.J. Adaptive Feature Zero Assisted Surrogate-Based EM Optimization for Microwave Filter Design. IEEE Microw. Wirel. Components Lett. 2019, 29, 2-4. [CrossRef] open in new tab
  21. Koziel, S.; Bekasiewicz, A. Multi-Objective Antenna Design by Means of Sequential Domain Patching. IEEE Antennas Wirel. Propag. Lett. 2016, 15, 1089-1092. [CrossRef] open in new tab
  22. Lee, L.H.; Chew, E.P.; Teng, S.; Goldsman, D. Finding the non-dominated Pareto set for multi-objective simulation models. IIE Trans. 2010, 42, 656-674. [CrossRef] open in new tab
  23. Kim, I.; De Weck, O. Adaptive weighted-sum method for bi-objective optimization: Pareto front generation. Struct. Multidiscip. Optim. 2004, 29, 149-158. [CrossRef] open in new tab
  24. Koziel, S.; Bekasiewicz, A. Multi-objective design optimization of antenna structures using sequential domain patching with automated patch size determination. Eng. Optim. 2017, 50, 218-234. [CrossRef] open in new tab
  25. Li, M.; Yang, S.; Liu, X. Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization. IEEE Trans. Cybern. 2014, 44, 2568-2584. [PubMed] open in new tab
  26. Deb, K.; Thiele, L.; Laumanns, M.; Zitzler, E. Scalable Multi-Objective Optimization Test Problems. In Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, HI, USA, 12-17 May 2002. open in new tab
  27. 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. Eng. 2018, 28, e21253. [CrossRef] open in new tab
  28. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). open in new tab
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