Optimal synthesis of linear antenna arrays using a harmony search algorithm

Güney, Kerïm; Onay, Murat

doi:10.1016/j.eswa.2011.06.015

Cited by 49 publications

(41 citation statements)

References 34 publications

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“…The radiation pattern obtained by BSA is given in Figure 9. In Table 3, the NDL and MSL values of the radiation pattern obtained by BSA are compared with those of the CLONALG [21], BA [29], HSA [35], and BFA [24]. As it can be seen from this table, the NDL and MSL values of the pattern obtained by BSA are better than the other compared algorithms.…”

Section: Phase Only Controlmentioning

confidence: 82%

“…Table 1 also shows the results of MTACO [13], BA [16], PGSA [26], HSA [35], and BFA [19]. It can be clearly observed from Table 1 that NDL, MSL, and DRR values of BSA are better than those of MTACO [13], BA [16], PGSA [26], HSA [35], and BFA [19]. Figure 4 shows the convergence curve.…”

Section: Amplitude Only Controlmentioning

confidence: 87%

“…The synthesis results of BSA are compared with the results of seventeen different algorithms, PSO [15,30], GA [30], MTACO [13], QPM [15], BFA [19,24], BA [16,29], CLONALG [21], PGSA [26], TSA [30], MA [30], NSGA-2 [30], MODE [30], MOEA/D-DE [30], CLPSO [31], SOA [32], HSA [35], and MVMO [43].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…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%

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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: Phase Only Controlmentioning

confidence: 82%

Section: Amplitude Only Controlmentioning

confidence: 87%

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Güney

Durmuş

2015

International Journal of Antennas and Propagation

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“…They include neural networks (NN) (Guney & Sarikaya, 2007;Merad, Bendimerad, Meriah, & Jennas, 2007), genetic algorithm (GA) (Harrou, Tassadit, & Bouyeddou, 2010;Tennant, Dawoud, & Anderson, 1994;Yan & Lu, 1997), simulated annealing (SA) (Ferreira & Ares, 1997), tabu search (Akdagli & Guney, 2003;Merad, Bendimerad, & Meriah, 2008), controlled random search algorithm (Merad, Bendimerad, & Meriah, 2006), chaotic 434 F. Harrou and M.N. Nounou particle swarm optimization (PSO) algorithm (Mandal, Das, Bhattacharjee, Bhattacharjee, & Ghoshal, 2010;Wang & Feng, 2010), pattern search (PSearch) (Güneş & Tokan, 2010), bees algorithm (Guney & Onay, 2010), and harmony search algorithm (Guney & Onay, 2011).…”

Section: Introductionmentioning

confidence: 99%

Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

Harrou

Nounou

2014

Systems Science & Control Engineering

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The evolution of modern wireless communications systems has dramatically increased the demand for antenna arrays. An antenna array with certain radiation characteristics is often desired. However, the actual radiation pattern of an antenna array changes when faults are introduced in the array. In this paper a statistical fault detection methodology based on the exponentially weighted moving average (EWMA) control scheme is proposed to detect possible faulty radiation patterns in linear antenna arrays. The proposed method detects the faults based on deviation in the radiation pattern from the desired ones. The difference between synthesized radiation pattern obtained using the Minimax algorithm and the measured pattern can be used as an indicator about the existence or absence of faults. To assess the fault detection abilities of the EWMA control scheme, three case studies are considered, one involving a complete failure in one element in the array, one involving partial failure in two elements, and one involving degradation caused by random noise due to interference and other factors. The simulation results for all cases show the effectiveness of the proposed EWMA fault detection method.

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Robust adaptive phase‐only beamforming algorithm for interference suppression

Sheng

Han

et al. 2014

Adaptive Control & Signal

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The adaptive phase-only beamforming technique is very important for suppressing interferences in phased array radar. However, similar to the conventional beamforming technique, which features variable magnitudes and phases, the performance of phase-only beamforming is severely degraded by steering direction and array geometry errors. No studies on this problem are available. In this paper, the problem of adequate robustness for adaptive phase-only beamformers is investigated, and a robust phase-only beamforming algorithm based on semidefinite relaxation is proposed. This algorithm can suppress interferences by minimizing the array output power and maintain the desired signal without distortion. Robust adaptive phase-only weighting is first converted into a non-convex quadratic optimization problem and then into a convex optimization problem by the semidefinite relaxation technique, which can be solved easily. Experimental results demonstrate that the new robust adaptive phase-only beamforming algorithm can significantly reduce performance degradation caused by various array errors.

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Optimal synthesis of linear antenna arrays using a harmony search algorithm

Cited by 49 publications

References 34 publications

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

Robust adaptive phase‐only beamforming algorithm for interference suppression

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