Multi-Objective Hybrid Optimization Algorithm for Design a Printed MIMO Antenna with n78 - 5G NR Frequency Band Applications
This study introduces a novel multi-objective optimization algorithm integrating Customized Mutated Particle Swarm Optimization (CM-PSO) and an innovative modified Genetic Algorithm (GA) using an unexplored merged chaotic map. The hybrid algorithm converges to desired results faster than CM-PSO and...
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| Vydáno v: | IEEE access Ročník 11; s. 1 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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IEEE
01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | This study introduces a novel multi-objective optimization algorithm integrating Customized Mutated Particle Swarm Optimization (CM-PSO) and an innovative modified Genetic Algorithm (GA) using an unexplored merged chaotic map. The hybrid algorithm converges to desired results faster than CM-PSO and modified GA without trapping in local minima. Validation is conducted by designing a single-element and simple-structure dipole antenna so that its optimized S 11 is better than -30 dB at the resonance frequency and covers the 3.3 to 3.8 GHz frequency band with S 11 < -10 dB. Certainly, the -30 dB and covering frequency band criteria can be modified in the proposed algorithm. In the algorithm, the isolation between elements of a quad-Multiple-Input/Multiple-Output antenna, constructed using optimized dipole antennas, is set to be less than -20 dB (changeable criteria) so that the smallest size can be achieved. Computer Simulation Technology (CST) Studio Suite carries out electromagnetic and high-frequency simulations, and the novel developed optimization algorithm in MATLAB determines what and how much parameter values need to be changed by CM-PSO or an innovative modified GA in order to enhance the antenna's S 11 result and its Impedance Bandwidth (IBW). The input parameters of the algorithm are the dimensions of the proposed antenna's elements, which significantly influence its performance. |
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| AbstractList | This study introduces a novel multi-objective optimization algorithm integrating Customized Mutated Particle Swarm Optimization (CM-PSO) and an innovative modified Genetic Algorithm (GA) using an unexplored merged chaotic map. The hybrid algorithm converges to desired results faster than CM-PSO and modified GA without trapping in local minima. Validation is conducted by designing a single-element and simple-structure dipole antenna so that its optimized S 11 is better than -30 dB at the resonance frequency and covers the 3.3 to 3.8 GHz frequency band with S 11 < -10 dB. Certainly, the -30 dB and covering frequency band criteria can be modified in the proposed algorithm. In the algorithm, the isolation between elements of a quad-Multiple-Input/Multiple-Output antenna, constructed using optimized dipole antennas, is set to be less than -20 dB (changeable criteria) so that the smallest size can be achieved. Computer Simulation Technology (CST) Studio Suite carries out electromagnetic and high-frequency simulations, and the novel developed optimization algorithm in MATLAB determines what and how much parameter values need to be changed by CM-PSO or an innovative modified GA in order to enhance the antenna's S 11 result and its Impedance Bandwidth (IBW). The input parameters of the algorithm are the dimensions of the proposed antenna's elements, which significantly influence its performance. This study introduces a novel multi-objective optimization algorithm integrating Customized Mutated Particle Swarm Optimization (CM-PSO) and an innovative modified Genetic Algorithm (GA) using an unexplored merged chaotic map. The hybrid algorithm converges to desired results faster than CM-PSO and modified GA without trapping in local minima. Validation is conducted by designing a single-element and simple-structure dipole antenna so that its optimized [Formula Omitted] is better than −30 dB at the resonance frequency and covers the 3.3 to 3.8 GHz frequency band with [Formula Omitted] dB. Certainly, the −30 dB and covering frequency band criteria can be modified in the proposed algorithm. In the algorithm, the isolation between elements of a quad-Multiple-Input/Multiple-Output antenna, constructed using optimized dipole antennas, is set to be less than −20 dB (changeable criteria) so that the smallest size can be achieved. Computer Simulation Technology (CST) Studio Suite carries out electromagnetic and high-frequency simulations, and the novel developed optimization algorithm in MATLAB determines what and how much parameter values need to be changed by CM-PSO or an innovative modified GA in order to enhance the antenna’s [Formula Omitted] result and its Impedance Bandwidth (IBW). The input parameters of the algorithm are the dimensions of the proposed antenna’s elements, which significantly influence its performance. This study introduces a novel multi-objective optimization algorithm integrating Customized Mutated Particle Swarm Optimization (CM-PSO) and an innovative modified Genetic Algorithm (GA) using an unexplored merged chaotic map. The hybrid algorithm converges to desired results faster than CM-PSO and modified GA without trapping in local minima. Validation is conducted by designing a single-element and simple-structure dipole antenna so that its optimized <tex-math notation="LaTeX">$S_{11}$ </tex-math> is better than −30 dB at the resonance frequency and covers the 3.3 to 3.8 GHz frequency band with <tex-math notation="LaTeX">$S_{11} < -10$ </tex-math> dB. Certainly, the −30 dB and covering frequency band criteria can be modified in the proposed algorithm. In the algorithm, the isolation between elements of a quad-Multiple-Input/Multiple-Output antenna, constructed using optimized dipole antennas, is set to be less than −20 dB (changeable criteria) so that the smallest size can be achieved. Computer Simulation Technology (CST) Studio Suite carries out electromagnetic and high-frequency simulations, and the novel developed optimization algorithm in MATLAB determines what and how much parameter values need to be changed by CM-PSO or an innovative modified GA in order to enhance the antenna's <tex-math notation="LaTeX">$S_{11}$ </tex-math> result and its Impedance Bandwidth (IBW). The input parameters of the algorithm are the dimensions of the proposed antenna's elements, which significantly influence its performance. |
| Author | Hosseini, Vahid Farhang, Yousef Majidzadeh, Kambiz Ghobadi, Changiz |
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| Cites_doi | 10.1109/TCSII.2022.3220542 10.1109/JMMCT.2020.3000563 10.1109/PIERS53385.2021.9694670 10.1016/j.aeue.2021.153687 10.1007/978-981-13-5758-9_19 10.1016/j.eswa.2021.115299 10.1109/TAP.2022.3161389 10.15866/irecap.v9i6.17352 10.1017/S1759078720001440 10.1016/j.aeue.2022.154161 10.1109/LAWP.2005.863618 10.1109/ICEET53442.2021.9659639 10.1016/j.amc.2017.05.014 10.1109/TSMC.2018.2884523 10.1016/j.aeue.2019.02.011 10.1049/iet-map.2014.0086 10.1109/TEVC.2017.2743016 10.1002/jnm.2549 10.1016/j.aeue.2021.154067 10.1016/j.aeue.2019.05.003 10.1016/j.aeue.2021.153696 10.1109/TAP.2010.2050425 10.1109/SPI57109.2023.10145532 |
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| SubjectTerms | Antenna radiation patterns Antennas Chaotic Map CM-PSO Algorithm Computer simulation Criteria Design optimization Dipole antennas Frequencies Genetic Algorithm Genetic algorithms Hybrid Optimization Algorithms Microstrip antennas MIMO Antennas Multiple objective analysis Optimization Optimization algorithms Parameter modification Particle swarm optimization Printed Antennas Resonant frequency Software algorithms |
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| Title | Multi-Objective Hybrid Optimization Algorithm for Design a Printed MIMO Antenna with n78 - 5G NR Frequency Band Applications |
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