A Hybrid Optimization Algorithm for the Synthesis of Sparse Array Pattern Diagrams
To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorit...
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| Vydané v: | Applied sciences Ročník 15; číslo 12; s. 6490 |
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01.06.2025
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| Abstract | To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations. |
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| AbstractList | To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations. |
| Audience | Academic |
| Author | Liu, Youzhi Huang, Linshu Xie, Xu Ye, Huijuan |
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| Cites_doi | 10.1109/OJAP.2021.3066310 10.1016/j.jnlest.2024.100276 10.3390/electronics14071326 10.1109/ACCESS.2021.3083487 10.1038/s41928-023-01068-x 10.1109/JSEN.2024.3516038 10.3390/photonics12030210 10.1109/ACCESS.2024.3437433 10.1063/5.0249586 10.1109/JSEN.2024.3422995 10.1109/LAWP.2020.3014366 10.5573/IEIESPC.2024.13.6.579 10.1007/s11042-023-17329-y 10.1109/TAP.2021.3060592 10.1109/TIM.2025.3547095 10.1109/LCOMM.2024.3350647 10.1016/j.heliyon.2024.e26337 10.3389/fmech.2023.1288171 10.3390/electronics13020369 10.1109/ACCESS.2021.3109171 10.1109/ACCESS.2022.3188276 |
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| SubjectTerms | Accuracy Algorithms Antennas (Electronics) Arrays Comparative analysis Exploitation Gene mutations genetic algorithm Genetic aspects Mathematical optimization Optimization algorithms particle swarm optimization quantum-behaved particle swarm optimization Radiation sidelobe level sparse antenna array Velocity |
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| Title | A Hybrid Optimization Algorithm for the Synthesis of Sparse Array Pattern Diagrams |
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