Low Side Lobe Level Linear Array Optimization using Evolutionary Algorithms : A Review (original) (raw)
Related papers
Side Lobe Level Reduction in Antenna Array Using Evolutionary Algorithms: A Review
2013
Antenna array design is one of the most imperative electromagnetic optimization problem of current interest. In the antenna arrays the side lobe level is main problem which causes waste of energy. In this paper, different evolutionary algorithms are presented for reduction of side lobe level in antenna array. The invasive weed optimization(IWO) algorithm outperform PSO, ACO, and GA based on metrics such as average final accuracy,side lobe level (SLL),directivity, convergence speed, and robustness.
Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm
Antenna array is formed by assembly of radiating elements in an electrical or geometrical configuration. In most cases the elements are identical. In this paper proposed a very simple and powerful method for the synthesis of linear array antenna and GA. This method reduced the desired level of side lobe level (SLL) as well as to steer the main beam at different-different angle. A new method for adaptive beam forming for a linear antenna arrays using genetic algorithm (GA) are also proposed. Aditya Sharma | Er. Praveen Kumar Patidar"Review on Linear Array Antenna with Minimum Side Lobe Level Using Genetic Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14544.pdf
Linear antenna array synthesis with invasive weed optimization algorithm
2009
Abstract Linear antenna array design is one of the most important electromagnetic optimization problems of current interest. This article describes the application of a recently developed metaheuristic algorithm, known as the invasive weed optimization (IWO), to optimize the spacing between the elements of the linear array to produce a radiation pattern with minimum side lobe level and null placement control.
Performance Analysis of Adaptive Linear Array Optimization by Utilizing the Genetic Algorithm
There are many theoretical and practical methods explained in literature to reduce the side lobe level and to improve the overall performance of the array antenna. In this article, by using the multi-objective genetic optimization approach, the synthesizing challenge covered in this work is determining the masses of the array antenna components which produce the best radiation characteristics with the least amount of side lobe. The adaptable evolutionary algorithm is shown to increase the performance by lowering the sidelobe level value under-30dB for most instances with 8 parameters and few MATLAB commands. This approach proved to be effective in enhancing the antennas array's performance.
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/design-of-low-side-lobe-level-and-narrow-beam-width-antenna-array-using-genetic-and-particle-swarm-optimization-methods https://www.ijert.org/research/design-of-low-side-lobe-level-and-narrow-beam-width-antenna-array-using-genetic-and-particle-swarm-optimization-methods-IJERTV4IS060877.pdf In antenna array design especially in point-to-point communications, it is frequently desirable, to achieve both a low side lobe level and a narrow beam width. Trying to reduce SLL, results in an increase of beam width and vice versa. Exciting an antenna array according to Chebyshev distribution gives us the smallest beam width for a given SLL and vice versa. But this is not the smallest beam width obtained for any other value of SLL. In this paper, using random stochastic methods like Genetic algorithm based optimization and Particle Swarm Optimization, the SLL and HPBW obtained for a Dolph-Chebyshev linear array designed for a specified SLL are further reduced resulting in a narrow main beam and low side lobe level array.
Minimization of Side Lobe Level for Linear Antenna Arrays Using Improved Particle Swarm Optimization
2017
The present paper describes minimization of side lobe levels of an linear array antenna using strong evolution algorithm is of particle swarm optimization (PSO). For linear, non linear optimizations and to solve general dimensional problems with high performance a newly discovered PSO used. The implementation and mathematical preprocessing of PSO is easy to analyze when compared with other evolutionary algorithms like simulated annealing and genetic algorithm. The synthesis is based on minimization of side lobe levels in required directions with optimum amplitude distributions for properly arranged antenna array using PSO. Two design examples are considered with even and odd number of elements for the synthesis with required goal.
2012 1st International Conference on Recent Advances in Information Technology (RAIT), 2012
Array antennas synthesis is one of the most important problems in the optimization of antenna and electromagnetics. In this paper, a recently developed metaheuristic algorithm, known as the Gravitational Search Algorithm (GSA), is employed for the pattern synthesis of linear and nonuniform planar antenna arrays with desired pattern nulls in the interfering directions and minimum side lobe level (SLL) by position-only optimization. Like other nature-inspired algorithms, GSA is also a population-based method and uses a population of solutions to proceed to a global solution. The results of GSA are validated by comparing them with the results obtained using particle swarm optimization (PSO) and some other algorithms reported in literature for linear and planar array. The side-lobe level and null depth obtained from gravitational search algorithm for planar array are improved up to −30 dB and −200 dB, respectively. The results reveal the superior performance of GSA to the other techniques for the design of linear and planar antenna arrays.
Archives of Electrical Engineering, 2016
This paper presents a new modified method for the synthesis of non-uniform linear antenna arrays. Based on the recently developed invasive weeds optimization technique (IWO), the modified invasive weeds optimization method (MIWO) uses the mutation process for the calculation of standard deviation (SD). Since the good choice of SD is particularly important in such algorithm, MIWO uses new values of this parameter to optimize the spacing between the array elements, which can improve the overall efficiency of the classical IWO method in terms of side lobe level (SLL) suppression and nulls control. Numerical examples are presented and compared to the existing array designs found in the literature, such as ant colony optimization (ACO), particle swarm optimization (PSO), and comprehensive learning PSO (CLPSO). Results show that MIWO method can be a good alternative in the design of non-uniform linear antenna array.
Constraint-Based Synthesis of Linear Antenna Array Using Modified Invasive Weed Optimization
Progress In Electromagnetics Research M, 2014
This paper presents a novel technique for the synthesis of unequally spaced linear antenna array. The modified Invasive Weed Optimization (IWO) algorithm is applied to optimize the antenna element positions for suppressing peak side lobe level (PSLL) and for achieving nulls in specified directions. The novelty of the proposed approach is in the application of a constraint-based static penalty function during optimization of the array. The static penalty function is able to put selective pressure on the PSLL, the first null beam width (FNBW) or the accurate null positioning as desired by the application at hand lending a high degree of flexibility to the synthesis process. Various design examples are considered and the obtained results are validated by comparing with the results obtained using Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Cat Swarm Optimization (CSO). Results demonstrate that the proposed method outperforms the previously published methods in terms of a significant reduction in peak side lobe level while maintaining strong nulls in desired directions. The flexibility and ease of implementation of the modified IWO algorithm in handling the constraints using static penalty function is evident from this analysis, showing the usefulness of the constraint based method in electromagnetic optimization problems.