A Comprehensive Review on Advanced Optimization Techniques for Antenna Design
DOI:
https://doi.org/10.71426/jmt.v3.i1.pp380-405Keywords:
Antenna Optimization, Evolutionary Algorithms, Swarm Intelligence, Multi-objective Optimization, Surrogate Modeling, Artificial Intelligence, Electromagnetic SimulationAbstract
The rapid evolution of wireless communication systems has transformed antenna design from a predominantly analytical engineering task into a nonlinear, high-dimensional, and multi-objective optimization problem. Emerging applications such as fifth-generation (5G) and sixth-generation (6G) wireless networks, massive multiple-input multiple-output (MIMO) systems, millimeter-wave communication, satellite constellations, vehicular radar, biomedical implants, wearable electronics, and Internet-of-Things platforms impose stringent requirements on antenna compactness, bandwidth, gain, radiation efficiency, polarization stability, beam steering capability, mutual coupling suppression, and fabrication tolerance. This review presents a systematic and analytical examination of optimization techniques used in antenna design, covering deterministic methods, evolutionary algorithms, swarm intelligence, multi-objective optimization, surrogate-assisted optimization, space mapping, robust optimization, and artificial intelligence-driven frameworks. Special emphasis is placed on the role of surrogate modeling, physics-informed learning, hybrid search strategies, and uncertainty-aware optimization in reducing simulation cost and improving design reliability. The review further identifies unresolved challenges related to scalability, interpretability, convergence assurance, manufacturing uncertainty, and real-time adaptive antenna systems. Finally, future research directions are outlined toward physics-guided, data-efficient, robust, and autonomous optimization frameworks for next-generation electromagnetic systems.
References
[1] Balanis CA. Antenna Theory: Analysis and Design. 4th ed. Hoboken: John Wiley & Sons; 2016. Available from: https://www.wiley.com/en-mx/shop/general-introductory-electrical-electronics-engineering/antenna-theory-analysis-and-design-4th-edition-p-9781118642061
[2] Christodoulou CG, Tawk Y, Lane SA, Erwin SR. Reconfigurable antennas for wireless and space applications. Proceedings of the IEEE. 2012;100(7):2250-2261. Available from: https://doi.org/10.1109/JPROC.2012.2188249
[3] Rappaport TS, Xing Y, Kanhere O, Ju S, Madanayake A, Mandal S, et al. Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond. IEEE Access. 2019;7:78729-78757. Available from: https://doi.org/10.1109/ACCESS.2019.2921522
[4] Pozar DM. Microwave Engineering. 4th ed. Hoboken: John Wiley & Sons; 2011. Available from: https://www.wiley.com/en-us/shop/general-introductory-electrical-electronics-engineering/microwave-engineering-4th-edition-p-9780470631553
[5] Werner DH, Ganguly S. An overview of fractal antenna engineering research. IEEE Antennas and Propagation Magazine. 2003;45(1):38-57. Available from: https://doi.org/10.1109/MAP.2003.1189650
[6] Caloz C, Itoh T. Electromagnetic Metamaterials: Transmission Line Theory and Microwave Applications. Hoboken: John Wiley & Sons; 2005. Available from: https://doi.org/10.1002/0471754323
[7] Petosa A. Dielectric Resonator Antenna Handbook. Artech House; 2007. Available from: https://us.artechhouse.com/Dielectric-Resonator-Antenna-Handbook-P1064.aspx
[8] Jin JM. The finite element method in electromagnetics. John Wiley & Sons; 2015. Available from: https://www.wiley.com/en-us/shop/general-introductory-electrical-electronics-engineering/the-finite-element-method-in-electromagnetics-3rd-edition-p-9781118571361
[9] Taflove A, Hagness SC. Computational Electrodynamics: The Finite-difference Time-domain Method. Artech House Antennas and Propagation Library. Artech House; 2005. Available from: https://books.google.co.in/books?id=n2ViQgAACAAJ
[10] Haupt RL, Werner DH. Anatomy of a Genetic Algorithm. In Genetic Algorithms in Electromagnetics. 2007. p. 29-43. Available from: https://doi.org/10.1002/9780470106280.ch2
[11] Koziel S, Ogurtsov S. Antenna design by simulation-driven optimization. Cham, Switzerland: Springer International Publishing; 2014. Available from: https://doi.org/10.1007/978-3-319-04367-8
[12] Liu B, Aliakbarian H, Ma Z, Vandenbosch GA, Gielen G, Excell P. An efficient method for antenna design optimization based on evolutionary computation and machine learning techniques. IEEE Transactions on Antennas and Propagation. 2014;62(1):7-18. Available from: https://doi.org/10.1109/TAP.2013.2283605
[13] Garnett R. Bayesian optimization. Cambridge University Press; 2023. Available from: https://www.cambridge.org/in/universitypress/subjects/computer-science/pattern-recognition-and-machine-learning/bayesian-optimization
[14] Deb K. Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction. Multi-objective Evolutionary Optimisation for Product Design and Manufacturing. London: Springer London; 2011. p. 3-34. Available from: https://doi.org/10.1007/978-0-85729-652-8_1
[15] Du KJ, Li JY, Wang H, Zhang J. Multi-objective multi-criteria evolutionary algorithm for multi-objective multi-task optimization. Complex & Intelligent Systems. 2023;9(2):1211-1228. Available from: https://doi.org/10.1007/s40747-022-00650-8
[16] Nocedal J, Wright SJ. Numerical Optimization. 2nd ed. New York: Springer; 2006. Available from: https://doi.org/10.1007/978-0-387-40065-5
[17] Boyd S, Vandenberghe L. Convex optimization. Cambridge University Press; 2004. Available from: https://www.cambridge.org/9780521833783
[18] Clerc M. Particle swarm optimization. John Wiley & Sons; 2006. Available from: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470612163
[19] Poli R, Kennedy J, Blackwell T. Particle swarm optimization: An overview. Swarm Intelligence. 2007;1(1):33-57. Available from: https://doi.org/10.1007/s11721-007-0002-0
[20] Yang XS. Nature-inspired optimization algorithms. Academic Press; 2021. Available from: https://sciencedirect.com/book/monograph/9780128219867
[21] Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer. Advances in Engineering Software. 2014;69:46-61. Available from: https://doi.org/10.1016/j.advengsoft.2013.12.007
[22] Yang XS. Firefly Algorithms for Multimodal Optimization. Stochastic Algorithms: Foundations and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg; 2009. p. 169-178. Available from: https://doi.org/10.1007/978-3-642-04944-6_14
[23] Hansen N. The CMA Evolution Strategy: A Comparing Review. Towards a new evolutionary computation: Advances in the estimation of distribution algorithms. 2006:75-102. Available from: https://doi.org/10.1007/3-540-32494-1_4
[24] Eiben AE, Smith JE. What is an Evolutionary Algorithm? Natural Computing Series. 2003. p. 15-35. Available from: https://doi.org/10.1007/978-3-662-05094-1_2
[25] Forrester A, Sobester A, Keane A. Engineering Design via Surrogate Modelling: A Practical Guide. John Wiley & Sons; 2008. Available from: https://doi.org/10.1002/9780470770801
[26] Jin Y. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation. 2011;1(2):61-70. Available from: https://doi.org/10.1016/j.swevo.2011.05.001
[27] Jin Y, Olhofer M, Sendhoff B. A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation. 2002;6(5):481-494. Available from: https://doi.org/10.1109/TEVC.2002.800884
[28] Bandler JW, Biernacki RM, Chen SH, Grobelny PA, Hemmers RH. Space mapping technique for electromagnetic optimization. IEEE Transactions on Microwave Theory and Techniques. 1994;42(12):2536-2544. Available from: https://doi.org/10.1109/22.339794
[29] Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: A review. Computers & Chemical Engineering. 2018;108:250-267. Available from: https://doi.org/10.1016/j.compchemeng.2017.09.017
[30] Snoek J, Larochelle H, Adams RP. Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems. 2012;25. Available from: https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html
[31] Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N. Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE. 2016;104(1):148-175. Available from: https://doi.org/10.1109/JPROC.2015.2494218
[32] Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics. 2019;378:686-707. Available from: https://doi.org/10.1016/j.jcp.2018.10.045
[33] Goodfellow I, Bengio Y, Courville A. Deep learning. Adaptive Computation and Machine Learning Series. MIT Press; 2017. Available from: https://mitpress.mit.edu/9780262035613/deep-learning/
[34] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-444. Available from: https://doi.org/10.1038/nature14539
[35] Sutton RS, Barto AG. Reinforcement Learning: An Introduction. IEEE Transactions on Neural Networks. 1998;9(5):1054. Available from: https://doi.org/10.1109/TNN.1998.712192
[36] Thrun S, Littman ML. Reinforcement learning: An introduction. AI Magazine. 2000;21(1):103. Available from: https://link.gale.com/apps/doc/A61573878/AONE
[37] Wu Q, Cao Y, Wang H, Hong W. Machine-learning-assisted optimization and its application to antenna designs: Opportunities and challenges. China Communications. 2020;17(4):152-164. Available from: https://doi.org/10.23919/JCC.2020.04.014
[38] Wang JC, Qi ZJ, Zhou QY, Wu JW, Liu S, Li HD, Dai JY, Cheng Q. Design of an Intelligent Metasurface with Independently and Precisely Tunable Amplitude-Phase Control. 2025 Nineteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials). 2025. p. X-353. Available from: https://doi.org/10.1109/Metamaterials65622.2025.11174187
[39] Jain R, Ramya R, Thakare VV, Singhal PK. Design and analysis of antenna through machine learning for next-generation IoT system. Discover Internet of Things. 2025;5(1):38. Available from: https://doi.org/10.1007/s43926-025-00126-4
[40] Kennedy J. Swarm intelligence. Handbook of Nature-Inspired and Innovative Computing: Integrating Classical Models with Emerging Technologies. Boston, MA: Springer US; 2006. p. 187-219. Available from: https://link.springer.com/chapter/10.1007/0-387-27705-6_6
[41] Holland JH. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press; 1992. Available from: https://doi.org/10.7551/mitpress/1090.001.0001
[42] Lampinen J, Storn R. Differential Evolution. New Optimization Techniques in Engineering. Studies in Fuzziness and Soft Computing. 2004. p. 123-166. Available from: https://doi.org/10.1007/978-3-540-39930-8_6
[43] Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation. 2002;6(1):58-73. Available from: https://doi.org/10.1109/4235.985692
[44] Poli R. Analysis of the Publications on the Applications of Particle Swarm Optimisation. Journal of Artificial Evolution and Applications. 2008;2008(1):685175. Available from: https://doi.org/10.1155/2008/685175
[45] Goudos SK, Diamantoulakis PD, Matin MA, Sarigiannidis P, Wan S, Karagiannidis GK. Design of antennas through artificial intelligence: State of the art and challenges. IEEE Communications Magazine. 2022;60(12):96-102. Available from: https://doi.org/10.1109/MCOM.006.2200124
[46] Gómez-Díaz JS, Perruisseau-Carrier J. Graphene-based plasmonic switches at near infrared frequencies. Optics Express. 2013;21(13):15490-15504. Available from: https://doi.org/10.1364/OE.21.015490
[47] Koziel S. Low-Cost Data-Driven Surrogate Modeling of Antenna Structures by Constrained Sampling. IEEE Antennas and Wireless Propagation Letters. 2017;16:461-464. Available from: https://doi.org/10.1109/LAWP.2016.2583474
[48] Agarwal PK, Ezra E, Fox K. Geometric Optimization Revisited. Computing and Software Science: State of the Art and Perspectives. Cham: Springer International Publishing; 2019. p. 66-84. Available from: https://link.springer.com/chapter/10.1007/978-3-319-91908-9_5
[49] Koziel S, Leifsson L. Surrogate-Based Modeling and Optimization. New York: Springer; 2013. Available from: https://doi.org/10.1007/978-1-4614-7551-4
[50] Queipo NV, Haftka RT, Shyy W, Goel T, Vaidyanathan R, Tucker PK. Surrogate-based analysis and optimization. Progress in Aerospace Sciences. 2005;41(1):1-28. Available from: https://doi.org/10.1016/j.paerosci.2005.02.001
[51] Simpson TW, Poplinski JD, Koch PN, Allen JK. Metamodels for Computer-based Engineering Design: Survey and recommendations. Engineering with Computers. 2001;17(2):129-150. Available from: https://doi.org/10.1007/PL00007198
[52] Frazier PI. A Tutorial on Bayesian Optimization. arXiv preprint arXiv:1807.02811. 2018. Available from: https://doi.org/10.48550/arXiv.1807.02811
[53] Wang X, Jin Y, Schmitt S, Olhofer M. Recent Advances in Bayesian Optimization. ACM Computing Surveys. 2023;55(13s):1-36. Available from: https://doi.org/10.1145/3582078
[54] Ben-Tal A, Nemirovski A. Robust optimization – methodology and applications. Mathematical Programming. 2002;92(3):453-480. Available from: https://link.springer.com/article/10.1007/s101070100286
[55] Campi MC, Garatti S. The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs. SIAM Journal on Optimization. 2008;19(3):1211-1230. Available from: https://doi.org/10.1137/07069821X
[56] Coello CA, Lamont GB, Veldhuizen DA. Evolutionary Algorithms for Solving Multi-Objective Problems. Boston, MA: Springer US; 2007. Available from: https://doi.org/10.1007/978-0-387-36797-2
[57] Zhang Q, Li H. MMOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation. 2007;11(6):712-731. Available from: https://doi.org/10.1109/TEVC.2007.892759
[58] Yang XS, Deb S. Cuckoo search via Lévy flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). 2009. p. 210-214. Available from: https://doi.org/10.1109/NABIC.2009.5393690
[59] Mirjalili S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications. 2016;27(4):1053-1073. Available from: https://doi.org/10.1007/s00521-015-1920-1
[60] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization. 2007;39(3):459-471. Available from: https://doi.org/10.1007/s10898-007-9149-x
[61] Arani MS, Shahidi R, Zhang L. A State-of-the-Art Survey on Advanced Electromagnetic Design: A Machine-Learning Perspective. IEEE Open Journal of Antennas and Propagation. 2024;5(4):1077-1094. Available from: https://doi.org/10.1109/OJAP.2024.3412609
[62] Eiben AE, Smith JE. Introduction to Evolutionary Computing. Natural Computing Series. Springer Berlin Heidelberg; 2015. Available from: https://doi.org/10.1007/978-3-662-05094-1
[63] Silver D, Huang A, Maddison CJ, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M, Dieleman S. Mastering the game of Go with deep neural networks and tree search. Nature. 2016;529(7587):484-489. Available from: https://doi.org/10.1038/nature16961
[64] LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. 2010 IEEE International Symposium on Circuits and Systems. 2010. p. 253-256. Available from: https://doi.org/10.1109/ISCAS.2010.5537907
[65] Christodoulou C, Georgiopoulos M. Applications of Neural Networks in Electromagnetics. Artech House, Inc.; 2000. Available from: https://dl.acm.org/doi/abs/10.5555/557934
[66] Kim JH, Choi SW. A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna. IEEE Access. 2020;8:226059-226063. Available from: https://doi.org/10.1109/ACCESS.2020.3045464
[67] Ar-Reyouchi EM, Hadj-Sadek A, Ghoumid K, Hage-Ali S, Elmazria O. Hybrid AI-driven optimization for real-time 6G ad hoc communications using fluid antenna systems. Physical Communication. 2025;102948. Available from: https://doi.org/10.1016/j.phycom.2025.102948
[68] Xu W, Liu J, Bai TQ, Wu H, Yang QS. The evolving landscape of AI-powered antenna design, optimization, and control: A comprehensive review. Ain Shams Engineering Journal. 2026;17(3):104052. Available from: https://doi.org/10.1016/j.asej.2026.104052
[69] Koziel S, Pietrenko-Dabrowska A, Leifsson L. Antenna optimization using machine learning with reduced-dimensionality surrogates. Scientific Reports. 2024;14(1):21567. Available from: https://doi.org/10.1038/s41598-024-72478-w
[70] Chen S, Sun GH, Wang K. Inverse design of microstrip antennas based on deep learning. Electronics. 2025;14(13):2510. Available from: https://doi.org/10.3390/electronics14132510
[71] Karahan EA, Shao Z, Sengupta K. Deep Learning Aided Modelling and Inverse Design for Multi-Port Antennas. 2024 IEEE International Symposium on Antennas and Propagation (APS/URSI). 2024. Available from: https://ieeexplore.ieee.org/document/10686871
[72] Karahan EA, Liu Z, Gupta A, Shao Z, Zhou J, Khankhoje U, Sengupta K. Deep-learning enabled generalized inverse design of multi-port radio-frequency and sub-terahertz passives and integrated circuits. Nature Communications. 2024;15(1):10734. Available from: https://doi.org/10.1038/s41467-024-54178-1
[73] Gajbhiye PA, Singh SP, Sharma MK. A comprehensive review of AI and machine learning techniques in antenna design optimization and measurement. Discover Electronics. 2025;2(1). Available from: https://doi.org/10.1007/s44291-025-00084-9
[74] Zhang C, Zou C, Guo S, Zhao Y, Shen T. Deep Learning-Based Inverse design of Stochastic-Topology Metamaterials for Radar Cross Section Reduction. Materials. 2025;18(21):4841. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC12609193/
[75] Chen ZN, Liu P, Kuang Y, Lyu Y, Zhang B, Yang D, et al. Deep Learning-Enabled Generative Antenna Design: Review and Prospects. IEEE Transactions on Antennas and Propagation. 2026;74(5):4051-4066. Available from: https://doi.org/10.1109/TAP.2026.3662776
[76] Helali H, Aidi M, Aguili T. Inverse design of frequency reconfigurable antenna arrays using machine learning. AEU - International Journal of Electronics and Communications. 2026;211:156310. Available from: https://doi.org/10.1016/j.aeue.2026.156310
[77] Kumari EK, Szymański JR, Żurek-Mortka M, Sathiyanarayanan M. Design and development of compact UWB-MIMO antenna with enhanced isolation for short distance V2X communications. Journal of Electromagnetic Waves and Applications. 2025;39(18):2173-2196. Available from: https://doi.org/10.1080/09205071.2025.2540027
[78] Melkeri VS, Jawali N, Kalnoor G. Design and development of 4×4 MIMO antennas for smart 5G devices. International Journal of Information Technology. 2021;13(4):1693-1698. Available from: https://doi.org/10.1007/s41870-021-00688-w
[79] Jawali NS, Melkeri VS, Kalnoor G. Design and Enhancement of MIMO Antennas for Smart 5G Devices. Microstrip Antenna Design for Wireless Applications. CRC Press; 2021. p. 169-181. Available from: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003093558-14
[80] Kumari EK, Szymański JR, Żurek-Mortka M, Sathiyanarayanan M. A novel high isolation quad-port multiband MIMO antenna for V2X applications at Sub-6 GHz band. Frequenz. 2025;79(7-8):385-402. Available from: https://doi.org/10.1515/freq-2024-0288
[81] Sharma PK, Szymański JR, Żurek-Mortka M, Sathiyanarayanan M, Sharma D. Design and analysis of four leaf clover shaped MIMO antenna for Sub-6 GHz V2X applications. Frequenz. 2024;78. Available from: https://doi.org/10.1515/freq-2023-0076
[82] Islamov IJ, Hasanov MH, Hunbataliyev EZ. Mathematical Modeling of the Antenna Devices of the Microwave Range. The International Conference on Artificial Intelligence and Applied Mathematics in Engineering. Cham: Springer International Publishing; 2022. p. 160-175. Available from: https://doi.org/10.1007/978-3-031-31956-3_14
[83] Islamov I. Optimization of broadband microstrip antenna device for 5G wireless communication systems. Transport and Telecommunication Journal. 2023;24(4):409-422. Available from: https://doi.org/10.2478/ttj-2023-0032
[84] Karthikeyan M, Sitharthan R, Ali T, Pathan S, Anguera J, Shanmuga Sundar D. Stacked T-Shaped Strips Compact Antenna for WLAN and WiMAX Applications. Wireless Personal Communications. 2022;123(2):1523-1536. Available from: https://doi.org/10.1007/s11277-021-09198-1
[85] Islamov I. Modeling of Microwave Antenna Systems. Modeling of Antenna and Waveguide Devices for Wireless and Satellite Communications Systems. Cham: Springer Nature Switzerland; 2024. p. 137-208. Available from: https://doi.org/10.1007/978-3-031-79010-2_2
[86] Gupta SK, Mehta S, Abougreen AN, Singh P. Antenna Identification and Power Allocation in Multicell Massive MIMO Downstream: Energy Conservation Under User Sum-Rate Constraint. Emerging Materials, Technologies, and Solutions for Energy Harvesting. IGI Global Scientific Publishing; 2024. p. 1-15. Available from: https://doi.org/10.4018/979-8-3693-2003-7.ch001
[87] Reddy P, Kasabegoudar VG. High Gain Multiband Microstrip Antenna for LTE, WLAN, Amateur Radio, and Sub-6 GHz 5G Applications. Progress In Electromagnetics Research C. 2023;136:87-99. Available from: https://doi.org/10.2528/PIERC23061102
[88] Sapkale P, Mehta S. Integration of Reconfigurable Intelligent Surfaces With Antennas for 6G Wireless Communications. Radar and RF Front End System Designs for Wireless Systems. IGI Global Scientific Publishing; 2024. p. 112-132. Available from: https://doi.org/10.4018/979-8-3693-0916-2.ch005
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Islam Islamov, Jerzy R. Szymański, Marta Żurek-Mortka, Mithileysh Sathiyanarayanan, Shilpa Mehta, Pradeep Reddy, Vidyadhar S Melkeri, Kusuma Kumari Emandi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Journal of Modern Technology (JMT) Journal operates under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This allows others distribute, remix, tweak, and build upon the work, even commercially, as long as they credit the authors for the original creation. All authors publishing in JMT Journal accept these as the terms of publication.
