Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-22T07:31:03.034Z Has data issue: false hasContentIssue false
  • English
  • Français

A comparison between GA, PSO, and IWO for shaped beam reflector antennas

Published online by Cambridge University Press:  30 June 2014

Seyed Mohammad Alavi  and
Ali F. Naini
Seyed Mohammad Alavi*
Affiliation:
Department of Electrical Engineering, Imam-Hossein Comprehensive University, Tehran, Iran
Ali F. Naini
Affiliation:
Department of Electrical and Computer Engineering, Shahed University, Tehran, Iran. Phone: + 98 21 51210
*
Corresponding author: Seyed Mohammad Alavi Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a comparison between three evolutionary algorithms (EAs) for pattern synthesis of offset reflector antenna fed by a planar array of horn antennas. To perform the optimization process, an elliptical-shaped beam in the U–V plane (U = sinθ cosφ and V = sinθ sinφ) is considered as the desired far-field radiation pattern. To attain the appropriate excitation value for array elements, three conditions are considered: (1) variable amplitude (with uniform phase distribution), (2) variable phase (with uniform amplitude distribution), and (3) variable amplitude and phase excitation. Obtaining the appropriate excitation value based on the mathematical methods is always complicated and time-consuming. Therefore, genetic algorithm (GA) and particle swarm optimization (PSO) as two well-known EAs have been used widely for different applications and shown the promise to solve complicated problems. This paper compares these two EAs with invasive weed optimization (IWO) which is robust and has simple and powerful process with few tuning parameters. We found that for pattern synthesis of multi-feed reflector antenna in different conditions, IWO can provide accurate and comparable results with GA and PSO methods at approximately same iteration number. The convergence diagrams as well as the optimized radiation patterns for different conditions are presented and compared for GA, PSO and IWO.

Keywords

EM field theory and numerical techniquesAntennas and propagation for wireless systems
Type
Research Paper
Information
International Journal of Microwave and Wireless Technologies , Volume 7 , Issue 5 , October 2015 , pp. 565 - 570
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

[1]Mandal, A.; Zafar, H.; Das, S.; Vasilakos, A.: A modified differential evolution algorithm for shaped beam linear array antenna design. Prog. Electromagn. Res., 125 (2012), 439–457.Google Scholar
[2]Bhattacharyya, A.K.: Projection matrix method for shaped beam synthesis in phased arrays and reflectors. IEEE Trans. Antennas Propag., 55 (3) (2007), 675–683.Google Scholar
[3]Botha, E.; McNamara, D.A.: A contoured beam synthesis technique for planar antenna arrays with quadrantal and centro-symmetry. IEEE Trans. Antennas Propag., 41 (9) (1993), 1222–1231.Google Scholar
[4]Foudazi, A.; Mallahzadeh, A.R.: Pattern synthesis of multi-feed reflector antenna using invasive weed optimization. IET Microw. Antennas Propag., 6 (14) (2013), 1583–1589.Google Scholar
[5]Foudazi, A.; Mallahzadeh, A.R.; Samadi-Taheri, M.M.: Pattern synthesis of multi-feed reflector antenna using iwo algorithm, in European Conf. on Antennas and Propagation, Czech, 2012.Google Scholar
[6]Venkataraman, P.: Applied Optimization with Matlab Programming, A Wiley–Interscience Publication, John Wiley & Sons, New York, 2001.Google Scholar
[7]Perini, J.: Note on antenna pattern synthesis using numerical iterative methods. IEEE Trans. Antennas Propag., 19 (2) (1971), 284–286.Google Scholar
[8]Oraizi, H.; Fallahpour, M.: Sum, difference and shaped beam pattern synthesis by non-uniform spacing and phase control. IEEE Trans. Antennas Propag., 59 (12) (2011), 4505–4511.CrossRefGoogle Scholar
[9]Haupt, R.L.; Werner, D.H.: Genetic Algorithms in Electromagnetics, Wiley–IEEE Press, April 2007, Hoboken, New Jersey.CrossRefGoogle Scholar
[10]Oraizi, H.; Fallahpour, M.: Nonuniformly spaced linear array design for the specified beamwidth/sidelobe level or specified directivity/sidelobe level with mutual coupling considerations. Prog. Electromagn. Res. M, 4 (2008), 185–209.CrossRefGoogle Scholar
[11]Jin, N.; Rahmat-Samii, Y.: Advances in particle swarm optimization for antenna designs real-number, binary, single-objective and multiobjective implementations. IEEE Trans. Antennas Propag., 55 (3) (2007), 556–567.Google Scholar
[12]Fallahpour, M.; Baumgartner, M.A.; Kothari, A.; Ghasr, M.T.; Pommerenke, D.; Zoughi, R.: Compact Ka-band one-port vector reflectometer using a wideband electronically controlled phase shifter. IEEE Trans. Instrum. Meas., 61 (10) (2012), 2807–2816.Google Scholar
[13]Karimkashi, S.; Kishk, A.A.: Invasive weed optimization and its features in electromagnetics. IEEE Trans. Antennas Propag., 58 (4) (2010), 1269–1278.CrossRefGoogle Scholar
[14]FEKO Suite 5.5, EM Software and Systems, 2011, http://www.feko.infoGoogle Scholar
[15]Chandrasekar, K.; Ramana, N.V.: Performance comparison of GA, DE, PSO and SA approaches in enhancement of total transfer capability using FACTS devices. J. Electr. Eng. Technol., 7 (4) (2012), 493–500.Google Scholar