Optimization of circular antenna arrays using particle swarm optimization (original) (raw)
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Design of linear and circular antenna arrays using cuckoo optimization algorithm
2014
The design of linear and circular antenna arrays is one of the important electromagnetic optimization problems. In this paper, the problem of designing these arrays for specific radiation properties is dealt with. The biogeography based optimization (BBO) method, which represents a new evolutionary algorithm, is used in the optimization process. BBO is used to minimize the maximum side lobe level (SLL) and null control for isotropic linear antenna arrays by optimizing different array parameters (position, amplitude, and phase). Similarly, for non-uniform circular antenna arrays (CAA), BBO is used to determine an optimum set of weights and positions that provide a radiation pattern with maximum SLL reduction with the constraint of a fixed major lobe beam width. The results obtained show the effectiveness of BBO compared to other optimization methods.
DESIGN OF NON-UNIFORM CIRCULAR ANTENNA ARRAYS USING PARTICLE SWARM OPTIMIZATION
2008
In this paper, the design of non-uniform circular antenna arrays with optimum side lobe level reduction is presented. The particle swarm optimization (PSO) method, which represents a new approach for optimization problems in electromagnetics, is used in the optimization process. The method of particle swarm optimization is used to determine an optimum set of weights and antenna element separations that provide a radiation pattern with maximum side lobe level reduction with the constraint of a fixed major lobe beamwidth. The results show that the design of non-uniform circular antenna arrays using PSO method provides a side lobe level reduction better than that obtained using genetic algorithms.
Optimisation of antenna arrays using the cuckoo search algorithm
The goal of this article is to introduce and use the cuckoo search (CS) as an optimisation algorithm for the electromagnetics and antenna community. The CS is a new nature-inspired evolutionary algorithm (EA) for solving Ndimensional optimisation problems. Compared with other nature-inspired algorithms, the CS algorithm is easy to understand and implement and has minimum number of parameters to tune. Different examples are presented that illustrate the use of the CS algorithm, and the results are compared with results obtained using other optimisation methods. Preliminary results suggest that the CS algorithm can in some cases outperform other EAs, at least for the examples studied in this article.
LINEAR AND CIRCULAR ARRAY OPTIMIZATION: A STUDY USING PARTICLE SWARM INTELLIGENCE
—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.
UNIFORMLY SPACED PLANAR ANTENNA ARRAY OPTIMIZATION USING CUCKOO SEARCH ALGORITHM
In this modern era a great deal of metamorphism is observed around us which eventuate due to some minute modifications and innovations in the area of Science and Technology. This paper deals with the application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design of an optimized planar antenna array which ensures high gain, directivity, suppression of side lobes, increased efficiency and improves other antenna parameters as well[1], [2] and [3].
International Journal of Artificial Intelligence & Applications, 2014
In this modern era a great deal of metamorphism is observed around us which eventuate due to some minute modifications and innovations in the area of Science and Technology. This paper deals with the application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design of an optimized planar antenna array which ensures high gain ,directivity, suppression of side lobes, increased efficiency and improves other antenna parameters as well[1], [2] and [3].
Synthesis of Circular Array Antennas Using Accelerated Particle Swarm Optimization
International Journal of Advanced Trends in Computer Science and Engineering, 2019
In this paper, the design and synthesis of Circular Antenna Array Configuration (CAAC) is performed using the Amplitude-Spacing technique. The synthesis process uses the novel evolutionary computing tool known as Accelerated Particle Swarm Optimization (APSO). A 25 element and 50 element CAAC is designed with objectives like sidelobe level (SLL) suppression with beam-width (BW) constraints. While mitigating the inter-element spacing, the constraint on the circumference of the circle is also imposed. Analysis based on the results in terms of radiation pattern in which the SLL and BW are computed. The whole simulation is carried out in MATLAB.
Procedia Computer Science, 2015
This paper describes the comparison of the performance of Circular array (CA) and Hexagonal Array (HA) of uniformly excited isotropic antennas which can generate directive beam with minimum relative Side Lobe Level (SLL). The Improved Particle Swarm Optimization (IPSO) method, which represents a novel approach for optimization problems in electromagnetic, is used in the optimization process. Two examples has been presented and solved. In first example, the IPSO is used to determine an optimal set of 'ON-OFF' elements in an 12 element thinned array, and in second example, IPSO is used to determine an optimal set of amplitude distributions in an 18 element array that provide a radiation pattern with maximum SLL reduction. This paper is basically concerned with the comparison of the performance of thinned CA and HA in terms of SLL by fixing the other array design variable. Numerical results for synthesizing two different array geometries demonstrated the superiority of Hexagonal Array over the Circular Array for both the examples.
Sidelobe level suppression is a typical circular array (CA) optimization problem. Many conventional techniques are proposed in the due course of time which are computationally complexed and time consuming. As an alternative, several evolutionary and heuristics approaches have evolved for solving such problems. In this paper, a novel accelerated particle swarm optimization (APSO) is considered for solving circular array optimization problem. The APSO is implemented for CA synthesis with the objective of SLL suppression. The objective is achieved under scanned and unscanned beam conditions. A very low SLL of -25dB is observed when the main beam positioned at 00,150 and 300 . In all the cases the efficiency of the APSO is evaluated by the comparing the generated radiation patterns with those obtained from uniform CA.
OUT PERFORMANCE OF CUCKOO SEARCH ALGORITHM AMONG NATURE INSPIRED ALGORITHMS IN PLANAR ANTENNA ARRAYS
In this modern era a great deal of metamorphism is observed around us which eventuate due to some minute modifications and innovations in the area of Science and Technology. This paper deals with the application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design of an optimized planar antenna array which ensures high gain ,directivity, suppression of side lobes, increased efficiency and improves other antenna parameters as well.