Linear antenna array synthesis using cat swarm optimization

Pappula, Lakshman; Ghosh, Debalina

doi:10.1016/j.aeue.2013.12.012

Cited by 126 publications

(99 citation statements)

References 17 publications

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“…Additionally, the metaheuristic algorithms are capable of escaping from local minima. Genetic algorithm (GA) [7-12, 20, 23, 25, 39], tabu search algorithm (TSA) [23,30], modified touring ant colony algorithm (MTACO) [13], particle swarm optimization (PSO) [15,30,33], bees algorithm (BA) [16,29], bacterial foraging algorithm (BFA) [19,24], clonal selection algorithm (CLONALG) [21], plant growth simulation algorithm (PGSA) [26], differential evolution (DE) algorithm [14,18,27], biogeography based optimization (BBO) [28], multiobjective DE (MODE) [30], memetic algorithm (MA) [17,23,30], nondominated sorting GA-2 (NSGA-2) [30], decomposition with DE (MOEA/D-DE) [30], comprehensive learning PSO (CLPSO) [31], seeker optimization algorithm (SOA) [32], invasive weed optimization (IWO) algorithm [34], harmony search algorithm (HSA) [35], firefly algorithm (FA) [36,38], cuckoo search (CS) algorithm [37,42], differential search algorithm (DSA) [40], cat swarm optimization (CSO) [41], and mean variance mapping optimization (MVMO) [43] can be given as the examples of these metaheuristic algorithms used for pattern nulling.…”

Section: Introductionmentioning

confidence: 99%

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Güney

Durmuş

2015

International Journal of Antennas and Propagation

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An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

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Section: Introductionmentioning

confidence: 99%

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Güney

Durmuş

2015

International Journal of Antennas and Propagation

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“…It has been observed that the HPBW is minimized up to 5° and the SLL is reduced up to -21.08 dB when N was increased up to 16. As compared with the results of the BBO [26] and the PSO [29] there is an improvement of 1.0 dB in the SLL and 2.1° in the HPBW by using the proposed algorithm shown in Fig. 7 Brazilian …”

Section: B Optimization Of Inter-element Spacingmentioning

confidence: 88%

Analysis of Linear Antenna Array for minimum Side Lobe Level, Half Power Beamwidth, and Nulls control using PSO

Rahman

Cao

Ahmed

et al. 2017

J. Microw. Optoelectron. Electromagn. Appl.

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“…PSO was applied based on the techniques used in [9] [10]. CSO and GE were applied using the techniques discussed in [10], and [20] respectively.…”

Section: Numerical Evaluationmentioning

confidence: 99%

“…CSO models the behavior of cats that rest mostly except when they are tracking some targets. Trying to find the next best position to move into, there are two modes in CSO: seeking mode and tracking mode [10].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Leaky Feeder Slot Spacing for Better Beam Forming in Mines and Tunnels

Hassan¹,

Farjow²,

Fernando³

2016

IJCNS

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The radiating cable (or leaky feeder) is the leading solution for distributed broadband wireless access in underground mines and tunnels. The traditional radiating cable has equally spaced slots along its axis radiating evenly. However, with the new requirement for tracking and localization of miners, this paper shows how to modify the leaky feeder to function as a beam forming antenna array by appropriately optimizing the slot spacing. Optimizing the leaky feeder slot distances using Genetic algorithm has a good impact on the channel parameters, making it an excellent choice for leaky feeder antennas. Linear, as well as bent practical leaky feeder shapes, were considered, and we tested five different optimization algorithms and compared side lobe energies, varying the slot distance which had an impact on side lobe level. Also, we derived the expression for the BER, spectral efficiency and throughput considering a time-varying wireless channel. We observed that as the radius of curvature of the feeder increased (approaching a straight line), the spectral efficiency decreased as expected. Interestingly different optimization algorithms perform differently at different carrier frequencies. An optimization method should be chosen according to the required goal to be achieved (maintaining uniform equal power along the cable, or a more directed beam, or using a certain frequency of operation).

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Linear antenna array synthesis using cat swarm optimization

Cited by 126 publications

References 17 publications

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Analysis of Linear Antenna Array for minimum Side Lobe Level, Half Power Beamwidth, and Nulls control using PSO

Optimization of Leaky Feeder Slot Spacing for Better Beam Forming in Mines and Tunnels

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