Determining the excitation coefficients of an array using genetic algorithms

Shimizu, Masashi

doi:10.1109/aps.1994.407697

Cited by 38 publications

(11 citation statements)

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“…Whether the search is deterministic or stochastic, it is possible to improve the reliability of the results. GA's are stochastic search mechanisms that utilize a Darwinian criterion of population evolution [12]- [14]. The GA has robustness that allows its structural functionality to be applied to many different search problems This effectively means that once the search variables are encoded into a suitable format, the GA scheme can be applied in many environments.…”

Section: Implementation Procedures Of Genetic Algorithmmentioning

confidence: 99%

Genetic and TABU algorithm based array synthesis for the generation of a shaped beam antenna

Subhashini

Kumar

Lalitha

2010

International Conference on Computer and Communication Engineering (ICCCE'10)

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Antenna designers are constantly challenged with the temptation to search for optimum solutions for the design of complex electromagnetic devices. The ability of using numerical methods to accurately and efficiently characterizing the relative quality of a particular design has excited the engineers to apply stochastic global evolutionary optimizers for this objective. Evolutionary techniques have been applied with growing applications to the design of complex electromagnetic systems. These schemes are finding popularity in electromagnetic as design tools and problem solvers because of their flexibility, veracity and ability to optimize in complex multimodal search spaces. This paper discusses the design and optimization of antenna arrays with special emphasis on Genetic Algorithm and TABU search method .It also illustrates the implementation procedure of Genetic algorithm and Tabu search algorithm in electromagnetic optimization problems. The optimization procedure is then used to design linear antenna array with specific array factor requirements. A comparative study of both design tools is done through simulation.

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Section: Implementation Procedures Of Genetic Algorithmmentioning

confidence: 99%

Genetic and TABU algorithm based array synthesis for the generation of a shaped beam antenna

Subhashini

Kumar

Lalitha

2010

International Conference on Computer and Communication Engineering (ICCCE'10)

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“…Evolutionary algorithms such as genetic algorithm [2,3], particle swarm optimization [4] have been successfully applied in the synthesis of array antenna. The authors [5] applied genetic algorithm for design of fully digital controlled reconfigurable array antenna.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of discrete amplitude-only linear array antenna with minimum side lobe level using iterative fast fourier transform

Singh

Mahanti

Pilli

2012

2012 5th International Conference on Computers and Devices for Communication (CODEC)

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This paper presents a method based on iterative fast fourier technique to minimize peak side lobe level of a linear array of uniformly spaced isotropic antennas by 3-bit digital attenuator. One example has been presented with 30 isotropic antennas. The synthesis is performed by varying only discrete amplitude values of a 3-bit attenuator. Pattern generated is broadside when all excitation phases of the elements are kept constant, i.e. zero degree. Array factor is related to element excitations via inverse Fourier transform for a linear array of isotropic antennas with uniform element spacing. This property is used in this method.

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“…Studies have been done on GA-based methods of reducing the sidelobes of an array, amplitude tapering or element position perturbation, planar array synthesis, etc. [4][5][6][7]. Particle swarm optimization is a recently invented high-performance optimizer that possesses several highly desirable attributes.…”

Section: Introductionmentioning

confidence: 99%

Design of a linear array antenna for shaped beam using genetic algorithm

Kundukulam

Beenamole

2008

Int J RF and Microwave Comp Aid Eng

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A linear array antenna design with desired radiation pattern has been presented based on genetic algorithm (GA) approach. Examples of cosecant and flat-topped beam patterns are illustrated to show the flexibility of GA to solve complex antenna synthesis problems by suitably selecting the fitness function, even with a simple GA. The results have been validated by IE3D electromagnetic simulation. The antenna arrays with different element geometries can also be implemented using the proposed technique. V V C 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE 18: 410-416, 2008.

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Determining the excitation coefficients of an array using genetic algorithms

Cited by 38 publications

References 0 publications

Genetic and TABU algorithm based array synthesis for the generation of a shaped beam antenna

Genetic and TABU algorithm based array synthesis for the generation of a shaped beam antenna

Synthesis of discrete amplitude-only linear array antenna with minimum side lobe level using iterative fast fourier transform

Design of a linear array antenna for shaped beam using genetic algorithm

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