Radiation Beam Pattern Synthesis of Concentric Circular Antenna Arrays Using Hybrid Approach Based on Cuckoo Search

Sun, Geng; Liu, Yanheng; Chen, Zhaoyu; Liang, Shuang; Wang, Aimin; Zhang, Ying

doi:10.1109/tap.2018.2846771

Cited by 56 publications

(30 citation statements)

References 39 publications

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“…For example, an optimization framework in the presence of mutual coupling was proposed in Bencivenni et al In Bui et al, a fast full‐wave scheme was proposed for large printed antenna arrays analysis by considering mutual coupling. Moreover, in our previous study, the mutual coupling between the antenna elements of a concentric circular antenna array was studied. According to these studies, mutual coupling will directly affect the beam patterns by, for example, increasing the maximum SLL or changing the directions of the nulls.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Lévy flight uses short‐distance search and occasional long‐distance walking alternately; this expands the search scope and increases the population diversity, thus avoiding local optima. The jumping path step length of Lévy flight is as follows:

L_{step} = α \oplus \frac{γ}{{(||, ξ)}^{1 / β}},

({, \begin{array}{c} γ \sim N ((),, 0, σ_{γ}^{2}) \\ ξ \sim N ((),, 0, σ_{ξ}^{2}) \end{array}),

({, \begin{array}{c} σ_{γ} = {\{\frac{normalΓ ((), 1 + β) italicsin ((), italicπβ / 2)}{normalΓ ([], ((), 1 + β) / 2) 2^{((), β - 1) / 2} β}\}}^{1 / β} \\ σ_{ξ} = 1 \end{array}),

where α is a weighting factor, γ and ξ are random numbers following a normal distribution as shown in Equation (9), σ γ and σ ξ are the corresponding SDs, as shown in Equation (10), β is a parameter of the probability density function of the Lévy distribution, with β ∈ [0, 2], and Γ( x ) is the standard Gamma distribution function.…”

Section: Proposed Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Zheng

Liu

Sun

et al. 2019

Int J Numerical Modelling

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Large‐scale antenna arrays (LSAAs) are used to achieve satisfactory performance in 5G communications and radar systems. However, sidelobe suppression and nulls control of LSAAs are high‐dimensional nonlinear optimization problems owing to the large number of antenna elements. In this study, we formulate an optimization problem for joint sidelobe suppression and nulls control of large‐scale linear antenna arrays and propose particle swarm optimization with global search and population mutation (PSOGP) to solve this problem. PSOGP introduces global search and population mutation operators into conventional particle swarm optimization to improve performance in terms of convergence rate and accuracy in large solution spaces. Simulation results demonstrate that compared with other methods, the proposed PSOGP has better overall performance in joint sidelobe suppression and nulls control.

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Section: Simulation Resultsmentioning

confidence: 99%

L_{step} = α \oplus \frac{γ}{{(||, ξ)}^{1 / β}},

({, \begin{array}{c} γ \sim N ((),, 0, σ_{γ}^{2}) \\ ξ \sim N ((),, 0, σ_{ξ}^{2}) \end{array}),

({, \begin{array}{c} σ_{γ} = {\{\frac{normalΓ ((), 1 + β) italicsin ((), italicπβ / 2)}{normalΓ ([], ((), 1 + β) / 2) 2^{((), β - 1) / 2} β}\}}^{1 / β} \\ σ_{ξ} = 1 \end{array}),

Section: Proposed Algorithmmentioning

confidence: 99%

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Zheng

Liu

Sun

et al. 2019

Int J Numerical Modelling

Self Cite

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“…As the optimization problems of antenna arrays become more and more complex, many nonlinear problems are ascribed to optimization models of discrete and continuous variables, such as the pattern synthesis of CCAA in reference [23]. To illustrate the practicality of the CABMO algorithm in designing antenna arrays in the real world, the proposed algorithm will be applied to optimize the structure of the VLF umbrella antenna array introduced in reference [35] by suppressing the mutual coupling.…”

Section: Closely Spaced Umbrella Antenna Arraysmentioning

confidence: 99%

“…Liu et al [22] proposed a more generalized version to synthesize unequally spaced arrays including mutual coupling. Sun et al [23] utilized a hybrid approach to suppress the maximum SLL of a large concentric circular antenna array.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Adaptive Butterfly Mating Optimization and Its Applications in Synthesis and Structure Optimization of Antenna Arrays

Liu

et al. 2019

International Journal of Antennas and Propagation

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A novel chaotic adaptive butterfly mating optimization (CABMO) is proposed to be used in synthesizing the beam pattern. In order to improve the optimization accuracy and avoid trapping in the local optimum, the homogeneous chaotic system and adaptive movement mechanism are combined into the proposed algorithm, where the initialization and redistribution of butterflies are chaotically dispersed with an adaptive movement closely related to the ultraviolet changes. After validating the performance of CABMO through several benchmark functions with different dimensions, the improved algorithm outperforms when compared to other state-of-the-art nature-inspired metaheuristic algorithms. The proposed algorithm is then used to understand any linear array problems in terms of the sidelobe reduction. Finally, a CABMO strategy is utilized to optimize the mutual coupling model of the closely spaced VLF umbrella arrays. Results show that the optimized structure has comfortably outperformed the original structure. Full scanning of wave positions is realized from 15 to 30 kHz. The synthesis patterns are close to the theoretical optimum. The optimized results of the radiation performance and synthesized patterns demonstrate that the pattern synthesis and antenna structure optimization based on the CABMO algorithm provides a novel idea for antenna array optimization.

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“…Various optimization methods have been reported to synthesize sparse circular arrays in [14][15][16][17][18][19][20][21][22], including analytical techniques and evolutionary algorithms. In [14] and [15], the vector mapping (VM) method and matrix mapping method were used to handle complicated constraints for linear and planar arrays, respectively.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Constraint Synthesis of Rotationally Symmetric Sparse Circular Arrays Using a Hybrid Algorithm

Wang¹,

Jiao²

2019

PIER M

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Rotationally symmetric sparse circular arrays are synthesized under multiple constraints. By combining the modified differential evolution algorithm based on the harmony search (in short HSDE) with the vector mapping (VM) method, a hybrid algorithm, called VM-HSDE, is proposed for synthesizing sparse circular arrays with low sidelobe levels. Due to the array's specific geometry, the number of optimization variables is reduced, and the constrained optimization problem is simplified. Moreover, infeasible solutions are avoided, and the problem is effectively solved by the VM-HSDE algorithm. Finally, three pattern optimization results verify the effectiveness and reliability of the VM-HSDE algorithm.

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Radiation Beam Pattern Synthesis of Concentric Circular Antenna Arrays Using Hybrid Approach Based on Cuckoo Search

Cited by 56 publications

References 39 publications

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Joint sidelobe suppression and nulls control of large‐scale linear antenna array using particle swarm optimization with global search and population mutation

Chaotic Adaptive Butterfly Mating Optimization and Its Applications in Synthesis and Structure Optimization of Antenna Arrays

Multiple-Constraint Synthesis of Rotationally Symmetric Sparse Circular Arrays Using a Hybrid Algorithm

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