Zobrazit v EDS

Improved Sparrow Search Algorithm for Sparse Array Optimization.

Uloženo v:
Podrobná bibliografie
Název: Improved Sparrow Search Algorithm for Sparse Array Optimization.
Autoři: Ji, Juanjuan, Su, Jie, Zhang, Lanfang, Zhang, Liuying, Jiang, Julang, Ma, Hongliang, Batt, Suhaib
Zdroj: Modelling & Simulation in Engineering; 4/17/2025, Vol. 2025, p1-16, 16p
Témata: OPTIMIZATION algorithms, SEARCH algorithms, ANTENNA arrays, DIFFERENTIAL evolution, PROBLEM solving
Abstrakt: The synthesis problem of the number of array elements, array element spacing, and array formation is widely concerned in sparse array optimization. The local optimum problem is still an urgent problem to be solved in the existing optimization algorithms. A sparse array optimization algorithm on improved sparrow search algorithm (ISSA) is proposed in this paper. Firstly, a probabilistic following strategy is proposed to optimize the following strategy of the sparrow search algorithm (SSA), and it can improve the global search capability of the algorithm. Secondly, the adaptive local search and Cauchy–Gaussian mutation are used to avoid falling into the local optimum situation, and more high‐quality areas are searched to improve the local extremum escape ability and convergence performance of the algorithm. Finally, the peak sidelobe level (PSLL) is used as the fitness function to adaptively optimize the position of the array elements. Experimental simulations show that the proposed approach has good main lobe response and low sidelobe response. In the sparse planar array, the sidelobe level decreases by −1.41 dB compared with the genetic algorithm (GA) and 0.69 dB lower than the SSA. In sparse linear array, the sidelobe level decreases by −1.09 dB compared with differential evolution algorithm and 0.40 dB lower than the SSA. The optimization of sparse arrays significantly enhances the accuracy and robustness of antenna array error estimation. [ABSTRACT FROM AUTHOR]
Copyright of Modelling & Simulation in Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
Popis
Abstrakt:The synthesis problem of the number of array elements, array element spacing, and array formation is widely concerned in sparse array optimization. The local optimum problem is still an urgent problem to be solved in the existing optimization algorithms. A sparse array optimization algorithm on improved sparrow search algorithm (ISSA) is proposed in this paper. Firstly, a probabilistic following strategy is proposed to optimize the following strategy of the sparrow search algorithm (SSA), and it can improve the global search capability of the algorithm. Secondly, the adaptive local search and Cauchy–Gaussian mutation are used to avoid falling into the local optimum situation, and more high‐quality areas are searched to improve the local extremum escape ability and convergence performance of the algorithm. Finally, the peak sidelobe level (PSLL) is used as the fitness function to adaptively optimize the position of the array elements. Experimental simulations show that the proposed approach has good main lobe response and low sidelobe response. In the sparse planar array, the sidelobe level decreases by −1.41 dB compared with the genetic algorithm (GA) and 0.69 dB lower than the SSA. In sparse linear array, the sidelobe level decreases by −1.09 dB compared with differential evolution algorithm and 0.40 dB lower than the SSA. The optimization of sparse arrays significantly enhances the accuracy and robustness of antenna array error estimation. [ABSTRACT FROM AUTHOR]
ISSN:16875591
DOI:10.1155/mse/5544548