Main Article Content
In this study, two bioinspired computation (BIC) techniques are discussed and applied to the project and synthesis of multilayer frequency selective surfaces (FSS) within the microwave band, specifically for C, X and Ku bands. The proposed BIC techniques consist of combining an artificial, general regression neural network to a genetic algorithm (GA) and a cuckoo search algorithm, respectively. The objective is to find the optimal values of separation between the investigated FSS. Numerical analysis of the electromagnetic properties of the device is made possible with the finite integration method (FIT) and validated through the finite element method (FEM), utilizing both softwares CST Microwave Studio and Ansys HFSS respectively. Thus, the BIC-optimized devices present good phase / angular stability for angles 10°, 20°, 30° and 40°, as well as being polarization independent. The cutoff frequencies to control the operating frequency range of the FSS, referring to transmission coefficient in decibels (dB), were obtained at a threshold of –10dB. Numerical results denote good accordance with measured data.
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.
Copyrights for articles published in IJIER journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.
A.H. Alavi, and A H. Gandomi, “A robust data mining approach for formulation of geotechnical engineering systems”, International Journal of Computer Aided Methods in Engineering-Engineering Computations, vol. 28, no. 3, 2011, pp. 242-74.
S. Ali, N. Abbadeni, M. Batouche, “Multidisciplinary computational intelligence techniques: applications in business, engineering, and medicine”, IGI Global Snippet, 2012.
M.C. Alcantara Neto, H.R.O. Ferreira, J.P.L. Araujo, F.J.B. Barros, A. Gomes Neto, M.O. Alencar, G.P.S. Cavalcante, “A new compact, broadband, and dual-band FSS for satellites applications, optimized through hybrid bioinspired multiobjective technique”, IET Microwaves, Antennas & Propagation, 2020, pp.1-9.
M.C. Alcantara Neto, J.P.L. Araujo, R.J.S. Mota, F.J.B. Barros, F.H. Ferreira, G.P.S. Cavalcante, B.C. Lyra, “Design and Synthesis of an Ultra Wide Band FSS for mm-Wave Application via General Regression Neural Network and Multiobjective Bat Algorithm”, Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 18, (4), 2019, pp. 530-544.
Z. Cui, R. Alex, R. Akerkar, X.S. Yang, “Recent advances on bioinspired computation”, The Scientific World Journal, 2014, pp. 1-3.
X. S. Yang, and S. Deb, “Cuckoo search via Lévy flights”, in: Proc. of World Congress on Nature & Biologically Inspired Computing (NaBic 2009), IEEE Publications, 2009, USA, pp. 2010-2014.
M.C. Alcantara Neto, J.P.L. Araujo, F.J.B. Barros, A.N. Silva, G.P.S. Cavalcante, A.G. D’Assuncao, “Bioinspired multiobjective synthesis of x-band FSS via general regression neural network and cuckoo search algorithm”, Microwave and Optical Technology Letters, 57, (10), 2015, pp. 2400-2405.
M.C. Alcântara Neto, F.J.B. Barros, J.P.L. Araújo, H.S. Gomes, G.P.S. Cavalcante, A.G. d’Assunção, “A metaheuristic hybrid optimization technique for designing broadband FSS”, SBMO/IEEE MTT-S Int. Microwave and Optoelectronics Conference (IMOC), Porto de Galinhas, Brazil, November 2015, pp. 3-6.
W.C. Araújo, H.W.C. Lins, A.G. d’Assunção Jr, J.L.G. Mederios, A.G. d’Assunção, “A bioinspired hybrid optimization algorithm for designing broadband frequency selective surfaces”, Microwave and Optical Technology Letters, 56, (2), 2013, pp. 329–333.
A. Hoorfar, “Evolutionary programming in electromagnetic optimization: a review”, IEEE Trans. Antenna and Propagation, 2007, pp. 523–537.
Munk, B. A., “Frequency Selective Surfaces: Theory and Design”, [S.l.]: John Wiley & Sons, Inc., 2000.
M. Lambea, M. A. Gonzalez, J. A. Encinar, and J. Zapata, “Analysis of frequency selective surfaces with arbitrarily shaped apertures by finite element method and generalized scattering matrix”, IEEE Antennas and Propagation Society International Symposium. [S.l.]: IEEE, v. 4, 1995, p. 1644–1647.
A. C. C. Lima, E. A. Parker, and R. J. Langley, “Tunable frequency selective surface using liquid substrates”, Electronics Letters, Institution of Engineering and Technology (IET), v. 30, n. 4, 1994, p. 281–282.
Y. G. Li, Y. C. Chan, T. S. Mok, and J. C. Vardaxoglou, “Analysis of frequency selective surfaces on biased ferrite substrate”, IEEE Antennas and Propagation Society International Symposium. Digest. [S.l.]: IEEE, 1995, p. 1636–1639.
D. Specht, “A general regression neural network”, IEEE Transactions on Neural Networks, 1991. Institute of Electrical and Electronics Engineers (IEEE), 1991, v. 2, n. 6, p. 568–576.
X.S. Yang and S. Deb, “Multiobjective cuckoo search for design optimization”, Computers & Operations Research, 2013. Elsevier BV, v. 40, n. 6, 2013, pp. 1616–1624.
E.A. Nadaraya, “On estimating regression” Theory of Probability & Its Applications, Society for Industrial & Applied Mathematics (SIAM), v. 9, n. 1, 1964, p. 141–142.
G.S. Watson, “Smooth regression analysis”, Sankhya: The Indian Journal of Statistics, Serie A, v. 26, n. 4, 1964, pp. 359–372.
J. H. Holland, “Adaptation in natural and artificial systems”, University of Michigan Press: Ann Arbor, MI, 1975.
D.E. Goldberg, “Genetic algorithms in search, optimization and machine learning”, Addison-Wesley, 1989.
D. Beasley, D. R. Bull, and R. Martin, “An overview of genetic algorithms: Part 1, fundamentals”, Univ. Comput., 1994, v. 15.
N.A. Kumar, “Efficient hierarchical hybrids parallel genetic algorithm for shortest path routing”, 2014 5th International Conference - Confluence The Next Generation Information Technology Summit (Confluence). [S.l.]: IEEE, 2014.
Z. Konfrist, “Parallel genetic algorithms: advances, computing trends, applications and perspectives”, 18th International Parallel and Distributed Processing Symposium, Proceedings. [S.l.]: IEEE, 2004.
M. Farshbaf, M.R. Feizi-Derakhshi, “Multi-objective optimization of graph partitioning using genetic algorithms”, Third International Conference on Advanced Engineering Computing and Applications in Sciences, Proceedings. [S.l.]: IEEE, 2009.
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, v. 6, n. 2, 2002, pp. 182–197.
X.S. Yang and S. Deb, “Cuckoo search: recent advances and applications”, Neural Computing and Applications, Springer Nature, v. 24, n. 1, 2013, pp. 169–174.