Ant Lion Optimization algorithm to control side lobe level and null depths in linear antenna arrays

Saxena, Prerna; Kothari, Ashwin

doi:10.1016/j.aeue.2016.07.008

Cited by 101 publications

(62 citation statements)

References 20 publications

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“…The process of determining the parameters of the antenna array to obtain the required antenna radiation pattern is known as pattern synthesis [3]. Pattern synthesis of antenna arrays can be carried out by careful control of above mentioned five parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Antlions commonly known as doodlebugs are net-winged insects whose names are derived from their unique hunting behavior and their favorite prey, namely ants. [16] The ALO is governed by the following rules:…”

Section: Ant-lion Optimization Algorithm (Alo)mentioning

confidence: 99%

“…Siddarth in [15] have applied IWO and optimized element spacing for minimizing side lobe level and null placement control. Prerna Saxena in [16] used ALO for optimizing antenna currents by suppressing peaks of SLL as well as Null placement in specified directions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimization of Linear Antennas - A Survey

Yerrola¹,

Spandana²

2017

IJCA

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Section: Introductionmentioning

confidence: 99%

Section: Ant-lion Optimization Algorithm (Alo)mentioning

confidence: 99%

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Optimization of Linear Antennas - A Survey

Yerrola¹,

Spandana²

2017

IJCA

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“…These proposed methods include game theory (Nie and Comaniciu, 2006), auction and pricing mechanism (Kloeck et al, 2005;Huang et al, 2006), graph coloring (Peng et al, 2006) and local bargaining (Cao and Zheng, 2005). The stochastic searching methods which have been inspired by social behavior of species and natural evolutions are referred as evolutionary algorithms (Saxena and Kothari, 2016). These algorithms have proved that they work more efficiently while dealing with discontinuous, noisy and differential problems.…”

Section: Literature Surveymentioning

confidence: 99%

Channel assignment using differential evolution algorithm in cognitive radio networks

Latif¹

2017

Int. j. adv. appl. sci.

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The emerging wireless applications have increased the demand of wireless spectrum significantly. Present spectrum assignment is static, due to which problem of spectrum scarcity has been raised. Cognitive Radio (CR) is a promising technology to deal with spectrum scarcity problem, which uses dynamic spectrum allocation to utilize the vacant spectrum. The CR intelligently scans the spectrum in its vicinity and search the vacant spectrum. The optimization of available spectrum is important research challenge in cognitive radio networks (CRNs). In this research work, we have optimized the spectrum utility of SUs using Differential Evolution (DE) algorithm in order to reduce the interference incurs to primary users (PUs) and as well as among the secondary users (SUs). Moreover, the results are compared with other evolutionary channel assignment algorithms like Fuzzy Logic Ant Colony System (FLACS) and Color Sensitive Graph Coding Method (CSGC). It has been observed that the results of proposed algorithms can further enhance the spectrum utility in CRNs in comparison to FLACS and CSGC.

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“…Reference [20] utilizes the social network optimization (SNO) algorithm to design the PAA, and the results are compared with the stud genetic algorithm. Saxena and Kothari [21] use the ant lion optimization (ALO) to suppress the maximum SLL and to control the deep nulls of the LAA. In reference [22], a hybrid approach called particle swarm optimization and gravitational search algorithm-explore (PSOGSA-E) is proposed to reduce the maximum SLL of the random antenna array.…”

Section: Introductionmentioning

confidence: 99%

An Antenna Array Sidelobe Level Reduction Approach through Invasive Weed Optimization

Sun

Liu

et al. 2018

International Journal of Antennas and Propagation

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The problems of synthesizing the beam patterns of the linear antenna array (LAA) and the circular antenna array (CAA) are addressed. First, an optimization problem is formulated for reducing the maximum sidelobe level (SLL) of the beam patterns. Then, the formulated problem is solved by using the invasive weed optimization (IWO) algorithm. Various simulations are performed to evaluate the effectiveness of the IWO algorithm for the synthesis of the beam patterns of the LAA and the CAA. The results show that IWO has a better performance in terms of the accuracy, the convergence rate, and the stability compared with other algorithms for the SLL reductions. Moreover, the electromagnetic simulation results also show that IWO achieves the best performance for the beam pattern synthesis of the antenna arrays in practical conditions.

show abstract

Ant Lion Optimization algorithm to control side lobe level and null depths in linear antenna arrays

Cited by 101 publications

References 20 publications

Optimization of Linear Antennas - A Survey

Optimization of Linear Antennas - A Survey

Channel assignment using differential evolution algorithm in cognitive radio networks

An Antenna Array Sidelobe Level Reduction Approach through Invasive Weed Optimization

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