Particle Swarm Optimization Versus Genetic Algorithms for Phased Array Synthesis

Boeringer, D.W.; Werner, Douglas H.

doi:10.1109/tap.2004.825102

Cited by 831 publications

(404 citation statements)

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“…Moreover, this problem has also been a central and well studied problem with a strong engineering background in the field of manufacturing and telecommunications science [3,4]. In order to solve this problem, many methods have been proposed to obtain the multi-pattern arrays in previous literatures [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Bee Colony for Design of a Reconfigurable Antenna Array With Discrete Phase Shifters

Li¹,

Zhao²,

Wang³

et al. 2012

PIER C

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Abstract-Multi-beam antenna arrays have important applications in the field of communications and radar. The reconfigurable design problem is to find the element in a sector pattern main beam with side lobes. The same excitation amplitudes applied to the array with zero-phase should be in a high directivity, low side lobe pencil shaped main beam. This paper presents a new method of designing a reconfigurable antenna with quantized phase excitations using an improved artificial bee colony, called IABC. Compared with subsequent quantization, experimental results indicate that the performance of the discrete realization of the phase-excitation value can be improved.

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Section: Introductionmentioning

confidence: 99%

Improved Artificial Bee Colony for Design of a Reconfigurable Antenna Array With Discrete Phase Shifters

Li¹,

Zhao²,

Wang³

et al. 2012

PIER C

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show abstract

“…During the last decades various optimization techniques like Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Bees Algorithm (BA) [1][2][3][4][5][6][7] in addition to many hybrid optimization methods [8][9][10] have been used for optimizing parameters in the antenna and antenna arrays problem. Each of these methods has its own pros and cons.…”

Section: Introductionmentioning

confidence: 99%

DESIGN OPTIMIZATION OF A BOW-TIE ANTENNA FOR 2.45 GHz RFID READERS USING A HYBRID BSO-NM ALGORITHM

Mahmoud¹

2010

PIER

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Abstract-Recently the Bacterial foraging optimization algorithm (BFA) has attracted a lot of attention as a high-performance optimizer. This paper presents a hybrid approach involving Bacterial Swarm Optimization (BSO) and Nelder-Mead (NM) algorithm. The proposed algorithm is used to design a bow-tie antenna for 2.45 GHz Radio Frequency Identification (RFID) readers. The antenna is analyzed completely using Method of Moments (MoM), then the MoM code is coupled with the BSO-NM algorithm to optimize the antenna. The simulated antenna and the optimization algorithm programs were implemented using MATLAB version 7.4. To verify the validity of numerical simulations, the results are compared with those obtained using Feko Software Suite 5.3.

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“…In order to validate the MO design method of time-modulated linear arrays, we undertake a twofold comparative study over three significant instantiations of the design problem involving 16, 32, and 64 elements linear array. Firstly we compare the three kinds of design methodologies for linear arrays: the time modulation, the nonuniform excitation [21,22] and the phase-position method [23] using MOEA/D-DE to achieve the design objectives in each case. This comparison reflects the superiority of the time-modulation method over the two others.…”

Section: Introductionmentioning

confidence: 99%

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

Pal¹,

Das²,

Basak³

2010

PIER B

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Abstract-This article proposes a Multi-objective Optimization (MO) framework for the design of time-modulated linear antenna arrays with ultra low maximum Side Lobe Level (SLL), maximum Side Band Level (SBL) and main lobe Beam Width between the First Nulls (BWFN). In contrast to the existing optimization-based methods that attempt to minimize a weighted sum of SLL, SBL, and BWFN, we treat these as three distinct objectives that are to be achieved simultaneously and use one of the best known MultiObjective Evolutionary Algorithms (MOEAs) of current interest called MOEA/D-DE (Decomposition based MOEA with Differential Evolution operator) to determine the best compromise among these three objectives. Unlike the single-objective approaches, the MO approach provides greater flexibility in the design by yielding a set of equivalent final solutions from which the user can choose one that attains a suitable trade-off margin as per requirements. We compared time-modulated antenna structures with other methods for linear array synthesis such as the excitation method and the phase-position synthesis method on the basis of the approximated Pareto Fronts

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Particle Swarm Optimization Versus Genetic Algorithms for Phased Array Synthesis

Cited by 831 publications

References 40 publications

Improved Artificial Bee Colony for Design of a Reconfigurable Antenna Array With Discrete Phase Shifters

Improved Artificial Bee Colony for Design of a Reconfigurable Antenna Array With Discrete Phase Shifters

DESIGN OPTIMIZATION OF A BOW-TIE ANTENNA FOR 2.45 GHz RFID READERS USING A HYBRID BSO-NM ALGORITHM

Design of Time-Modulated Linear Arrays With a Multi-Objective Optimization Approach

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