Design of Yagi–Uda antennas using comprehensive learning particle swarm optimisation

Baskar, S.; Alphones, Arokiaswami; Suganthan, Ponnuthurai Nagaratnam; Liang, Jing

doi:10.1049/ip-map:20045087

Cited by 66 publications

(56 citation statements)

References 14 publications

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“…In Table 3, we show the results obtained by our PDA, designed using SED, compared with the results obtained by: (Baskar et al, 2005), who used PSO to optimize the element spacing and lengths of a YagiUda antenna; (Goudos et al, 2010) who used Generalized Differential Evolution applied to Yagi-Uda antenna design; (Li, 2007), who used Differential Evolution to optimize the geometric parameters of YagiUda antennas; (Yan et al, 2010), who designed a wide-band Yagi-Uda antenna with X-shape driven dipoles and parasitic elements using differential evolution algorithm, obtaining a bandwidth of 20%. Both (Baskar et al, 2005), (Goudos et al, 2010) and (Li, 2007) decide to perform the optimization only at the center frequency, and this is a simpler task and can lead to better results than an optimization over the whole antenna bandwidth, which is the choice we made in our SED design.…”

Section: Antennamentioning

confidence: 99%

“…Both (Baskar et al, 2005), (Goudos et al, 2010) and (Li, 2007) decide to perform the optimization only at the center frequency, and this is a simpler task and can lead to better results than an optimization over the whole antenna bandwidth, which is the choice we made in our SED design. Nonetheless, the results obtained by SED are better than the ones obtained by PSO and DE even at the center frequency.…”

Section: Antennamentioning

confidence: 99%

“…Therefore, an antenna design has been faced at different levels, from simple formulas (Collin, 1985) to sophisticated synthesis techniques (Orchard et al, 1985;Bucci et al, 1994), and from simple heuristic models (Carrel, 1961) to modern global random optimizations, such as GA (Linden & Altshuler, 1996Jones & Joines, 1997) and PSO (Baskar et al, 2005), with their heavy computational loads.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Structure-Based Evolutionary Design Applied to Wire Antennas

Casula¹,

Mazzarella²

2012

Genetic Programming - New Approaches and Successful Applications

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Section: Antennamentioning

confidence: 99%

Section: Antennamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Structure-Based Evolutionary Design Applied to Wire Antennas

Casula¹,

Mazzarella²

2012

Genetic Programming - New Approaches and Successful Applications

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“…Since its invention, continuous efforts have been put in optimizing its design for desired gain, impedance, SLL and bandwidth, etc., requirements using different optimization techniques based on traditional mathematical approaches [24,4,8,25,7,6,9] and Artificial Intelligence (AI) techniques [16,35,34,3,19,31,30].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Gain-Impedance Optimization of Yagi-Uda Antenna using NSBBO and NSPSO

Singh¹,

Mittal²,

Sachdeva³

2012

IJCA

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Biogeography-Based Optimization (BBO) is a population based algorithm which has shown impressive performance over other Evolutionary Algorithms (EAs). BBO algorithm is based on the study of distribution of biological organisms over space and time. YagiUda antenna design is most widely used antenna at VHF and UHF frequencies due to high gain, directivity and ease of construction. However, designing a Yagi-Uda antenna, that involves determination of optimal wire-lengths and their spacings, is highly complex and non-linear engineering problem. It further complicates as multiple objectives, viz. gain, and impedance, etc., are required to be optimized due to their conflicting nature, i.e., reactive antenna impedance increases significantly as antenna gain is intended to increase. In this paper Non-dominated Sorting BBO (NSBBO) is proposed and where standard and blended variants of BBO are investigated in optimizing six-element Yagi-Uda antenna designs for multiple objectives, viz., gain and impedance, where ranking of potential solutions is done using non-dominated sorting. The simulation results of BBO variants and Particle Swarm Optimization (PSO) are presented in the ending sections of the paper that depict clearly that NSBBO with blended migration operator is best option among all.

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“…Also, [41] shows that PSO algorithm convergence is faster than GA and SA for the same problem and the main computational time is lower than SA, binary GA, real GA, binary hybrid GA, and real hybrid GA. The literature on the use of the PSO method in the design of antenna arrays is extensive, a sample of which can be found in [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. In this paper, the method of PSO is used to provide a comprehensive study of the design of linear and circular antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

Linear and Circular Array Optimization: A Study Using Particle Swarm Intelligence

Al-Aqeel

2009

PIER B

128

114

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Abstract-Linear and circular arrays are optimized using the particle swarm optimization (PSO) method. Also, arrays of isotropic and cylindrical dipole elements are considered.The parameters of isotropic arrays are elements excitation amplitude, excitation phase and locations, while for dipole array the optimized parameters are elements excitation amplitude, excitation phase, location, and length. PSO is a high-performance stochastic evolutionary algorithm used to solve N -dimensional problems. The method of PSO is used to determine a set of parameters of antenna elements that provide the goal radiation pattern. The effectiveness of PSO for the design of antenna arrays is shown by means of numerical results. Comparison with other methods is made whenever possible. The results reveal that design of antenna arrays using the PSO method provides considerable enhancements compared with the uniform array and the synthesis obtained from other optimization techniques.

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Design of Yagi–Uda antennas using comprehensive learning particle swarm optimisation

Cited by 66 publications

References 14 publications

Structure-Based Evolutionary Design Applied to Wire Antennas

Structure-Based Evolutionary Design Applied to Wire Antennas

Multi-objective Gain-Impedance Optimization of Yagi-Uda Antenna using NSBBO and NSPSO

Linear and Circular Array Optimization: A Study Using Particle Swarm Intelligence

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